How to Validate Anagrams using Python with Example

In this tutorial , We’ll learn how to validate anagrams using Python. The validation of anagram words is one of the favourite questions in coding interviews. The idea is to write an algorithm to check if the input word creates a meaningful word when rearranged. So to validate an anagram using Python, we need to input two words and check if word1 in any case matches word2 after rearranging the words.

For example, the words “hello” and “ehllo”, let’s say that the word1 here is “hello”, so we need to write an algorithm to check whether we can make the word “ehllo” after rearranging the letters of the word “hello”. Below is how we can validate anagrams using Python:

def anagram(word1, word2):
    word1 = word1.lower()
    word2 = word2.lower()
    return sorted(word1) == sorted(word2)

print(anagram("hello", "ehllo"))
print(anagram("jacks", "ajcks"))
print(anagram("python", "pthon"))

Output

True
True
False

In the above code, I started with writing a Python function as “anagram” which includes two parameters(word1, word2). Now while initializing the words, I converted them to lower case then I am checking if the word1 equals the word2 after sorting both the words.

#python

What is GEEK

Buddha Community

Lawrence Lesch

1677668905

TS-mockito: Mocking Library for TypeScript

TS-mockito

Mocking library for TypeScript inspired by http://mockito.org/

1.x to 2.x migration guide

Main features

Strongly typed
IDE autocomplete
Mock creation (mock) (also abstract classes) #example
Spying on real objects (spy) #example
Changing mock behavior (when) via:
- thenReturn - return value #example
- thenThrow - throw an error #example
- thenCall - call custom method #example
- thenResolve - resolve promise #example
- thenReject - rejects promise #example
Checking if methods were called with given arguments (verify)
- anything, notNull, anyString, anyOfClass etc. - for more flexible comparision
- once, twice, times, atLeast etc. - allows call count verification #example
- calledBefore, calledAfter - allows call order verification #example
Resetting mock (reset, resetCalls) #example, #example
Capturing arguments passed to method (capture) #example
Recording multiple behaviors #example
Readable error messages (ex. 'Expected "convertNumberToString(strictEqual(3))" to be called 2 time(s). But has been called 1 time(s).')

Installation

npm install ts-mockito --save-dev

Usage

Basics

// Creating mock
let mockedFoo:Foo = mock(Foo);

// Getting instance from mock
let foo:Foo = instance(mockedFoo);

// Using instance in source code
foo.getBar(3);
foo.getBar(5);

// Explicit, readable verification
verify(mockedFoo.getBar(3)).called();
verify(mockedFoo.getBar(anything())).called();

Stubbing method calls

// Creating mock
let mockedFoo:Foo = mock(Foo);

// stub method before execution
when(mockedFoo.getBar(3)).thenReturn('three');

// Getting instance
let foo:Foo = instance(mockedFoo);

// prints three
console.log(foo.getBar(3));

// prints null, because "getBar(999)" was not stubbed
console.log(foo.getBar(999));

Stubbing getter value

// Creating mock
let mockedFoo:Foo = mock(Foo);

// stub getter before execution
when(mockedFoo.sampleGetter).thenReturn('three');

// Getting instance
let foo:Foo = instance(mockedFoo);

// prints three
console.log(foo.sampleGetter);

Stubbing property values that have no getters

Syntax is the same as with getter values.

Please note, that stubbing properties that don't have getters only works if Proxy object is available (ES6).

Call count verification

// Creating mock
let mockedFoo:Foo = mock(Foo);

// Getting instance
let foo:Foo = instance(mockedFoo);

// Some calls
foo.getBar(1);
foo.getBar(2);
foo.getBar(2);
foo.getBar(3);

// Call count verification
verify(mockedFoo.getBar(1)).once();               // was called with arg === 1 only once
verify(mockedFoo.getBar(2)).twice();              // was called with arg === 2 exactly two times
verify(mockedFoo.getBar(between(2, 3))).thrice(); // was called with arg between 2-3 exactly three times
verify(mockedFoo.getBar(anyNumber()).times(4);    // was called with any number arg exactly four times
verify(mockedFoo.getBar(2)).atLeast(2);           // was called with arg === 2 min two times
verify(mockedFoo.getBar(anything())).atMost(4);   // was called with any argument max four times
verify(mockedFoo.getBar(4)).never();              // was never called with arg === 4

Call order verification

// Creating mock
let mockedFoo:Foo = mock(Foo);
let mockedBar:Bar = mock(Bar);

// Getting instance
let foo:Foo = instance(mockedFoo);
let bar:Bar = instance(mockedBar);

// Some calls
foo.getBar(1);
bar.getFoo(2);

// Call order verification
verify(mockedFoo.getBar(1)).calledBefore(mockedBar.getFoo(2));    // foo.getBar(1) has been called before bar.getFoo(2)
verify(mockedBar.getFoo(2)).calledAfter(mockedFoo.getBar(1));    // bar.getFoo(2) has been called before foo.getBar(1)
verify(mockedFoo.getBar(1)).calledBefore(mockedBar.getFoo(999999));    // throws error (mockedBar.getFoo(999999) has never been called)

Throwing errors

let mockedFoo:Foo = mock(Foo);

when(mockedFoo.getBar(10)).thenThrow(new Error('fatal error'));

let foo:Foo = instance(mockedFoo);
try {
    foo.getBar(10);
} catch (error:Error) {
    console.log(error.message); // 'fatal error'
}

Custom function

You can also stub method with your own implementation

let mockedFoo:Foo = mock(Foo);
let foo:Foo = instance(mockedFoo);

when(mockedFoo.sumTwoNumbers(anyNumber(), anyNumber())).thenCall((arg1:number, arg2:number) => {
    return arg1 * arg2; 
});

// prints '50' because we've changed sum method implementation to multiply!
console.log(foo.sumTwoNumbers(5, 10));

Resolving / rejecting promises

You can also stub method to resolve / reject promise

let mockedFoo:Foo = mock(Foo);

when(mockedFoo.fetchData("a")).thenResolve({id: "a", value: "Hello world"});
when(mockedFoo.fetchData("b")).thenReject(new Error("b does not exist"));

Resetting mock calls

You can reset just mock call counter

// Creating mock
let mockedFoo:Foo = mock(Foo);

// Getting instance
let foo:Foo = instance(mockedFoo);

// Some calls
foo.getBar(1);
foo.getBar(1);
verify(mockedFoo.getBar(1)).twice();      // getBar with arg "1" has been called twice

// Reset mock
resetCalls(mockedFoo);

// Call count verification
verify(mockedFoo.getBar(1)).never();      // has never been called after reset

You can also reset calls of multiple mocks at once resetCalls(firstMock, secondMock, thirdMock)

Resetting mock

Or reset mock call counter with all stubs

// Creating mock
let mockedFoo:Foo = mock(Foo);
when(mockedFoo.getBar(1)).thenReturn("one").

// Getting instance
let foo:Foo = instance(mockedFoo);

// Some calls
console.log(foo.getBar(1));               // "one" - as defined in stub
console.log(foo.getBar(1));               // "one" - as defined in stub
verify(mockedFoo.getBar(1)).twice();      // getBar with arg "1" has been called twice

// Reset mock
reset(mockedFoo);

// Call count verification
verify(mockedFoo.getBar(1)).never();      // has never been called after reset
console.log(foo.getBar(1));               // null - previously added stub has been removed

You can also reset multiple mocks at once reset(firstMock, secondMock, thirdMock)

Capturing method arguments

let mockedFoo:Foo = mock(Foo);
let foo:Foo = instance(mockedFoo);

// Call method
foo.sumTwoNumbers(1, 2);

// Check first arg captor values
const [firstArg, secondArg] = capture(mockedFoo.sumTwoNumbers).last();
console.log(firstArg);    // prints 1
console.log(secondArg);    // prints 2

You can also get other calls using first(), second(), byCallIndex(3) and more...

Recording multiple behaviors

You can set multiple returning values for same matching values

const mockedFoo:Foo = mock(Foo);

when(mockedFoo.getBar(anyNumber())).thenReturn('one').thenReturn('two').thenReturn('three');

const foo:Foo = instance(mockedFoo);

console.log(foo.getBar(1));    // one
console.log(foo.getBar(1));    // two
console.log(foo.getBar(1));    // three
console.log(foo.getBar(1));    // three - last defined behavior will be repeated infinitely

Another example with specific values

let mockedFoo:Foo = mock(Foo);

when(mockedFoo.getBar(1)).thenReturn('one').thenReturn('another one');
when(mockedFoo.getBar(2)).thenReturn('two');

let foo:Foo = instance(mockedFoo);

console.log(foo.getBar(1));    // one
console.log(foo.getBar(2));    // two
console.log(foo.getBar(1));    // another one
console.log(foo.getBar(1));    // another one - this is last defined behavior for arg '1' so it will be repeated
console.log(foo.getBar(2));    // two
console.log(foo.getBar(2));    // two - this is last defined behavior for arg '2' so it will be repeated

Short notation:

const mockedFoo:Foo = mock(Foo);

// You can specify return values as multiple thenReturn args
when(mockedFoo.getBar(anyNumber())).thenReturn('one', 'two', 'three');

const foo:Foo = instance(mockedFoo);

console.log(foo.getBar(1));    // one
console.log(foo.getBar(1));    // two
console.log(foo.getBar(1));    // three
console.log(foo.getBar(1));    // three - last defined behavior will be repeated infinity

Possible errors:

const mockedFoo:Foo = mock(Foo);

// When multiple matchers, matches same result:
when(mockedFoo.getBar(anyNumber())).thenReturn('one');
when(mockedFoo.getBar(3)).thenReturn('one');

const foo:Foo = instance(mockedFoo);
foo.getBar(3); // MultipleMatchersMatchSameStubError will be thrown, two matchers match same method call

Mocking interfaces

You can mock interfaces too, just instead of passing type to mock function, set mock function generic type Mocking interfaces requires Proxy implementation

let mockedFoo:Foo = mock<FooInterface>(); // instead of mock(FooInterface)
const foo: SampleGeneric<FooInterface> = instance(mockedFoo);

Mocking types

You can mock abstract classes

const mockedFoo: SampleAbstractClass = mock(SampleAbstractClass);
const foo: SampleAbstractClass = instance(mockedFoo);

You can also mock generic classes, but note that generic type is just needed by mock type definition

const mockedFoo: SampleGeneric<SampleInterface> = mock(SampleGeneric);
const foo: SampleGeneric<SampleInterface> = instance(mockedFoo);

Spying on real objects

You can partially mock an existing instance:

const foo: Foo = new Foo();
const spiedFoo = spy(foo);

when(spiedFoo.getBar(3)).thenReturn('one');

console.log(foo.getBar(3)); // 'one'
console.log(foo.getBaz()); // call to a real method

You can spy on plain objects too:

const foo = { bar: () => 42 };
const spiedFoo = spy(foo);

foo.bar();

console.log(capture(spiedFoo.bar).last()); // [42]

Thanks

Szczepan Faber (https://www.linkedin.com/in/szczepiq)
Sebastian Konkol (https://www.linkedin.com/in/sebastiankonkol)
Clickmeeting (http://clickmeeting.com)
Michał Stocki (https://github.com/michalstocki)
Łukasz Bendykowski (https://github.com/viman)
Andrey Ermakov (https://github.com/dreef3)
Markus Ende (https://github.com/Markus-Ende)
Thomas Hilzendegen (https://github.com/thomashilzendegen)
Johan Blumenberg (https://github.com/johanblumenberg)

Download Details:

Author: NagRock
Source Code: https://github.com/NagRock/ts-mockito
License: MIT license

#typescript #testing #mock

Chloe Butler

1667425440

Pdf2gerb: Perl Script Converts PDF Files to Gerber format

pdf2gerb

Perl script converts PDF files to Gerber format

Pdf2Gerb generates Gerber 274X photoplotting and Excellon drill files from PDFs of a PCB. Up to three PDFs are used: the top copper layer, the bottom copper layer (for 2-sided PCBs), and an optional silk screen layer. The PDFs can be created directly from any PDF drawing software, or a PDF print driver can be used to capture the Print output if the drawing software does not directly support output to PDF.

The general workflow is as follows:

Design the PCB using your favorite CAD or drawing software.
Print the top and bottom copper and top silk screen layers to a PDF file.
Run Pdf2Gerb on the PDFs to create Gerber and Excellon files.
Use a Gerber viewer to double-check the output against the original PCB design.
Make adjustments as needed.
Submit the files to a PCB manufacturer.

Please note that Pdf2Gerb does NOT perform DRC (Design Rule Checks), as these will vary according to individual PCB manufacturer conventions and capabilities. Also note that Pdf2Gerb is not perfect, so the output files must always be checked before submitting them. As of version 1.6, Pdf2Gerb supports most PCB elements, such as round and square pads, round holes, traces, SMD pads, ground planes, no-fill areas, and panelization. However, because it interprets the graphical output of a Print function, there are limitations in what it can recognize (or there may be bugs).

See docs/Pdf2Gerb.pdf for install/setup, config, usage, and other info.

pdf2gerb_cfg.pm

#Pdf2Gerb config settings:
#Put this file in same folder/directory as pdf2gerb.pl itself (global settings),
#or copy to another folder/directory with PDFs if you want PCB-specific settings.
#There is only one user of this file, so we don't need a custom package or namespace.
#NOTE: all constants defined in here will be added to main namespace.
#package pdf2gerb_cfg;

use strict; #trap undef vars (easier debug)
use warnings; #other useful info (easier debug)


##############################################################################################
#configurable settings:
#change values here instead of in main pfg2gerb.pl file

use constant WANT_COLORS => ($^O !~ m/Win/); #ANSI colors no worky on Windows? this must be set < first DebugPrint() call

#just a little warning; set realistic expectations:
#DebugPrint("${\(CYAN)}Pdf2Gerb.pl ${\(VERSION)}, $^O O/S\n${\(YELLOW)}${\(BOLD)}${\(ITALIC)}This is EXPERIMENTAL software.  \nGerber files MAY CONTAIN ERRORS.  Please CHECK them before fabrication!${\(RESET)}", 0); #if WANT_DEBUG

use constant METRIC => FALSE; #set to TRUE for metric units (only affect final numbers in output files, not internal arithmetic)
use constant APERTURE_LIMIT => 0; #34; #max #apertures to use; generate warnings if too many apertures are used (0 to not check)
use constant DRILL_FMT => '2.4'; #'2.3'; #'2.4' is the default for PCB fab; change to '2.3' for CNC

use constant WANT_DEBUG => 0; #10; #level of debug wanted; higher == more, lower == less, 0 == none
use constant GERBER_DEBUG => 0; #level of debug to include in Gerber file; DON'T USE FOR FABRICATION
use constant WANT_STREAMS => FALSE; #TRUE; #save decompressed streams to files (for debug)
use constant WANT_ALLINPUT => FALSE; #TRUE; #save entire input stream (for debug ONLY)

#DebugPrint(sprintf("${\(CYAN)}DEBUG: stdout %d, gerber %d, want streams? %d, all input? %d, O/S: $^O, Perl: $]${\(RESET)}\n", WANT_DEBUG, GERBER_DEBUG, WANT_STREAMS, WANT_ALLINPUT), 1);
#DebugPrint(sprintf("max int = %d, min int = %d\n", MAXINT, MININT), 1); 

#define standard trace and pad sizes to reduce scaling or PDF rendering errors:
#This avoids weird aperture settings and replaces them with more standardized values.
#(I'm not sure how photoplotters handle strange sizes).
#Fewer choices here gives more accurate mapping in the final Gerber files.
#units are in inches
use constant TOOL_SIZES => #add more as desired
(
#round or square pads (> 0) and drills (< 0):
    .010, -.001,  #tiny pads for SMD; dummy drill size (too small for practical use, but needed so StandardTool will use this entry)
    .031, -.014,  #used for vias
    .041, -.020,  #smallest non-filled plated hole
    .051, -.025,
    .056, -.029,  #useful for IC pins
    .070, -.033,
    .075, -.040,  #heavier leads
#    .090, -.043,  #NOTE: 600 dpi is not high enough resolution to reliably distinguish between .043" and .046", so choose 1 of the 2 here
    .100, -.046,
    .115, -.052,
    .130, -.061,
    .140, -.067,
    .150, -.079,
    .175, -.088,
    .190, -.093,
    .200, -.100,
    .220, -.110,
    .160, -.125,  #useful for mounting holes
#some additional pad sizes without holes (repeat a previous hole size if you just want the pad size):
    .090, -.040,  #want a .090 pad option, but use dummy hole size
    .065, -.040, #.065 x .065 rect pad
    .035, -.040, #.035 x .065 rect pad
#traces:
    .001,  #too thin for real traces; use only for board outlines
    .006,  #minimum real trace width; mainly used for text
    .008,  #mainly used for mid-sized text, not traces
    .010,  #minimum recommended trace width for low-current signals
    .012,
    .015,  #moderate low-voltage current
    .020,  #heavier trace for power, ground (even if a lighter one is adequate)
    .025,
    .030,  #heavy-current traces; be careful with these ones!
    .040,
    .050,
    .060,
    .080,
    .100,
    .120,
);
#Areas larger than the values below will be filled with parallel lines:
#This cuts down on the number of aperture sizes used.
#Set to 0 to always use an aperture or drill, regardless of size.
use constant { MAX_APERTURE => max((TOOL_SIZES)) + .004, MAX_DRILL => -min((TOOL_SIZES)) + .004 }; #max aperture and drill sizes (plus a little tolerance)
#DebugPrint(sprintf("using %d standard tool sizes: %s, max aper %.3f, max drill %.3f\n", scalar((TOOL_SIZES)), join(", ", (TOOL_SIZES)), MAX_APERTURE, MAX_DRILL), 1);

#NOTE: Compare the PDF to the original CAD file to check the accuracy of the PDF rendering and parsing!
#for example, the CAD software I used generated the following circles for holes:
#CAD hole size:   parsed PDF diameter:      error:
#  .014                .016                +.002
#  .020                .02267              +.00267
#  .025                .026                +.001
#  .029                .03167              +.00267
#  .033                .036                +.003
#  .040                .04267              +.00267
#This was usually ~ .002" - .003" too big compared to the hole as displayed in the CAD software.
#To compensate for PDF rendering errors (either during CAD Print function or PDF parsing logic), adjust the values below as needed.
#units are pixels; for example, a value of 2.4 at 600 dpi = .0004 inch, 2 at 600 dpi = .0033"
use constant
{
    HOLE_ADJUST => -0.004 * 600, #-2.6, #holes seemed to be slightly oversized (by .002" - .004"), so shrink them a little
    RNDPAD_ADJUST => -0.003 * 600, #-2, #-2.4, #round pads seemed to be slightly oversized, so shrink them a little
    SQRPAD_ADJUST => +0.001 * 600, #+.5, #square pads are sometimes too small by .00067, so bump them up a little
    RECTPAD_ADJUST => 0, #(pixels) rectangular pads seem to be okay? (not tested much)
    TRACE_ADJUST => 0, #(pixels) traces seemed to be okay?
    REDUCE_TOLERANCE => .001, #(inches) allow this much variation when reducing circles and rects
};

#Also, my CAD's Print function or the PDF print driver I used was a little off for circles, so define some additional adjustment values here:
#Values are added to X/Y coordinates; units are pixels; for example, a value of 1 at 600 dpi would be ~= .002 inch
use constant
{
    CIRCLE_ADJUST_MINX => 0,
    CIRCLE_ADJUST_MINY => -0.001 * 600, #-1, #circles were a little too high, so nudge them a little lower
    CIRCLE_ADJUST_MAXX => +0.001 * 600, #+1, #circles were a little too far to the left, so nudge them a little to the right
    CIRCLE_ADJUST_MAXY => 0,
    SUBST_CIRCLE_CLIPRECT => FALSE, #generate circle and substitute for clip rects (to compensate for the way some CAD software draws circles)
    WANT_CLIPRECT => TRUE, #FALSE, #AI doesn't need clip rect at all? should be on normally?
    RECT_COMPLETION => FALSE, #TRUE, #fill in 4th side of rect when 3 sides found
};

#allow .012 clearance around pads for solder mask:
#This value effectively adjusts pad sizes in the TOOL_SIZES list above (only for solder mask layers).
use constant SOLDER_MARGIN => +.012; #units are inches

#line join/cap styles:
use constant
{
    CAP_NONE => 0, #butt (none); line is exact length
    CAP_ROUND => 1, #round cap/join; line overhangs by a semi-circle at either end
    CAP_SQUARE => 2, #square cap/join; line overhangs by a half square on either end
    CAP_OVERRIDE => FALSE, #cap style overrides drawing logic
};
    
#number of elements in each shape type:
use constant
{
    RECT_SHAPELEN => 6, #x0, y0, x1, y1, count, "rect" (start, end corners)
    LINE_SHAPELEN => 6, #x0, y0, x1, y1, count, "line" (line seg)
    CURVE_SHAPELEN => 10, #xstart, ystart, x0, y0, x1, y1, xend, yend, count, "curve" (bezier 2 points)
    CIRCLE_SHAPELEN => 5, #x, y, 5, count, "circle" (center + radius)
};
#const my %SHAPELEN =
#Readonly my %SHAPELEN =>
our %SHAPELEN =
(
    rect => RECT_SHAPELEN,
    line => LINE_SHAPELEN,
    curve => CURVE_SHAPELEN,
    circle => CIRCLE_SHAPELEN,
);

#panelization:
#This will repeat the entire body the number of times indicated along the X or Y axes (files grow accordingly).
#Display elements that overhang PCB boundary can be squashed or left as-is (typically text or other silk screen markings).
#Set "overhangs" TRUE to allow overhangs, FALSE to truncate them.
#xpad and ypad allow margins to be added around outer edge of panelized PCB.
use constant PANELIZE => {'x' => 1, 'y' => 1, 'xpad' => 0, 'ypad' => 0, 'overhangs' => TRUE}; #number of times to repeat in X and Y directions

# Set this to 1 if you need TurboCAD support.
#$turboCAD = FALSE; #is this still needed as an option?

#CIRCAD pad generation uses an appropriate aperture, then moves it (stroke) "a little" - we use this to find pads and distinguish them from PCB holes. 
use constant PAD_STROKE => 0.3; #0.0005 * 600; #units are pixels
#convert very short traces to pads or holes:
use constant TRACE_MINLEN => .001; #units are inches
#use constant ALWAYS_XY => TRUE; #FALSE; #force XY even if X or Y doesn't change; NOTE: needs to be TRUE for all pads to show in FlatCAM and ViewPlot
use constant REMOVE_POLARITY => FALSE; #TRUE; #set to remove subtractive (negative) polarity; NOTE: must be FALSE for ground planes

#PDF uses "points", each point = 1/72 inch
#combined with a PDF scale factor of .12, this gives 600 dpi resolution (1/72 * .12 = 600 dpi)
use constant INCHES_PER_POINT => 1/72; #0.0138888889; #multiply point-size by this to get inches

# The precision used when computing a bezier curve. Higher numbers are more precise but slower (and generate larger files).
#$bezierPrecision = 100;
use constant BEZIER_PRECISION => 36; #100; #use const; reduced for faster rendering (mainly used for silk screen and thermal pads)

# Ground planes and silk screen or larger copper rectangles or circles are filled line-by-line using this resolution.
use constant FILL_WIDTH => .01; #fill at most 0.01 inch at a time

# The max number of characters to read into memory
use constant MAX_BYTES => 10 * M; #bumped up to 10 MB, use const

use constant DUP_DRILL1 => TRUE; #FALSE; #kludge: ViewPlot doesn't load drill files that are too small so duplicate first tool

my $runtime = time(); #Time::HiRes::gettimeofday(); #measure my execution time

print STDERR "Loaded config settings from '${\(__FILE__)}'.\n";
1; #last value must be truthful to indicate successful load


#############################################################################################
#junk/experiment:

#use Package::Constants;
#use Exporter qw(import); #https://perldoc.perl.org/Exporter.html

#my $caller = "pdf2gerb::";

#sub cfg
#{
#    my $proto = shift;
#    my $class = ref($proto) || $proto;
#    my $settings =
#    {
#        $WANT_DEBUG => 990, #10; #level of debug wanted; higher == more, lower == less, 0 == none
#    };
#    bless($settings, $class);
#    return $settings;
#}

#use constant HELLO => "hi there2"; #"main::HELLO" => "hi there";
#use constant GOODBYE => 14; #"main::GOODBYE" => 12;

#print STDERR "read cfg file\n";

#our @EXPORT_OK = Package::Constants->list(__PACKAGE__); #https://www.perlmonks.org/?node_id=1072691; NOTE: "_OK" skips short/common names

#print STDERR scalar(@EXPORT_OK) . " consts exported:\n";
#foreach(@EXPORT_OK) { print STDERR "$_\n"; }
#my $val = main::thing("xyz");
#print STDERR "caller gave me $val\n";
#foreach my $arg (@ARGV) { print STDERR "arg $arg\n"; }

Download Details:

Author: swannman
Source Code: https://github.com/swannman/pdf2gerb

License: GPL-3.0 license

#perl

Shardul Bhatt

1626775355

Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities.

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly.

5 Reasons to Utilize Python for Programming Web Apps

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions.

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events.

Simple to read and compose

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building.

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties.

Utilized by the best

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player.

Massive community support

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions.

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking.

Progressive applications

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential.

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

Darius Greenholt

1597751700

How To Compare Tesla and Ford Company By Using Magic Methods in Python

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…

You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.

1. init

class AnyClass:
    def __init__():
        print("Init called on its own")
obj = AnyClass()

The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.

The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.

Init called on its own

2. add

Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,

class AnyClass:
    def __init__(self, var):
        self.some_var = var
    def __add__(self, other_obj):
        print("Calling the add method")
        return self.some_var + other_obj.some_var
obj1 = AnyClass(5)
obj2 = AnyClass(6)
obj1 + obj2

#python3 #python #python-programming #python-web-development #python-tutorials #python-top-story #python-tips #learn-python

Jade Bird

1666770774

Variables in Python

In this Python tutorial for beginners, we learn about Variables in Python. Variables are containers for storing data values. A Python variable is a symbolic name that is a reference or pointer to an object.

Code in GitHub: https://github.com/AlexTheAnalyst/PythonYouTubeSeries/blob/main/Python%20Basics%20101%20-%20Variables.ipynb

Creating Variables

Python has no command for declaring a variable.

A variable is created the moment you first assign a value to it.

Example

x = 5
y = "John"
print(x)
print(y)

Variables do not need to be declared with any particular type, and can even change type after they have been set.

Example

x = 4       # x is of type int
x = "Sally" # x is now of type str
print(x)

Casting

If you want to specify the data type of a variable, this can be done with casting.

Example

x = str(3)    # x will be '3'
y = int(3)    # y will be 3
z = float(3)  # z will be 3.0

Get the Type

You can get the data type of a variable with the type() function.

Example

x = 5
y = "John"
print(type(x))
print(type(y))

Single or Double Quotes?

String variables can be declared either by using single or double quotes:

Example

x = "John"
# is the same as
x = 'John'

Case-Sensitive

Variable names are case-sensitive.

Example

This will create two variables:

a = 4
A = "Sally"
#A will not overwrite a

Python Variables: How to Define/Declare String Variable Types

What is a Variable in Python?

A Python variable is a reserved memory location to store values. In other words, a variable in a python program gives data to the computer for processing.

Python Variable Types

Every value in Python has a datatype. Different data types in Python are Numbers, List, Tuple, Strings, Dictionary, etc. Variables in Python can be declared by any name or even alphabets like a, aa, abc, etc.

In this tutorial, we will learn,

How to Declare and use a Variable
Re-declare a Variable
Concatenate Variables
Local & Global Variables
Delete a variable

How to Declare and use a Variable

Let see an example. We will define variable in Python and declare it as “a” and print it.

a=100 
print (a)

Re-declare a Variable

You can re-declare Python variables even after you have declared once.

Here we have Python declare variable initialized to f=0.

Later, we re-assign the variable f to value “guru99”

Python 2 Example

# Declare a variable and initialize it
f = 0
print f
# re-declaring the variable works
f = 'guru99'
print f

Python 3 Example

# Declare a variable and initialize it
f = 0
print(f)
# re-declaring the variable works
f = 'guru99'
print(f)

Python String Concatenation and Variable

Let’s see whether you can concatenate different data types like string and number together. For example, we will concatenate “Guru” with the number “99”.

Unlike Java, which concatenates number with string without declaring number as string, while declaring variables in Python requires declaring the number as string otherwise it will show a TypeError

Variables in Python

For the following code, you will get undefined output –

a="Guru"
b = 99
print a+b

Once the integer is declared as string, it can concatenate both “Guru” + str(“99”)= “Guru99” in the output.

a="Guru"
b = 99
print(a+str(b))

Python Variable Types: Local & Global

There are two types of variables in Python, Global variable and Local variable. When you want to use the same variable for rest of your program or module you declare it as a global variable, while if you want to use the variable in a specific function or method, you use a local variable while Python variable declaration.

Let’s understand this Python variable types with the difference between local and global variables in the below program.

Let us define variable in Python where the variable “f” is global in scope and is assigned value 101 which is printed in output
Variable f is again declared in function and assumes local scope. It is assigned value “I am learning Python.” which is printed out as an output. This Python declare variable is different from the global variable “f” defined earlier
Once the function call is over, the local variable f is destroyed. At line 12, when we again, print the value of “f” is it displays the value of global variable f=101

Variables in Python

Python 2 Example

# Declare a variable and initialize it
f = 101
print f
# Global vs. local variables in functions
def someFunction():
# global f
    f = 'I am learning Python'
    print f
someFunction()
print f

Python 3 Example

# Declare a variable and initialize it
f = 101
print(f)
# Global vs. local variables in functions
def someFunction():
# global f
    f = 'I am learning Python'
    print(f)
someFunction()
print(f)

While Python variable declaration using the keyword global, you can reference the global variable inside a function.

Variable “f” is global in scope and is assigned value 101 which is printed in output
Variable f is declared using the keyword global. This is NOT a local variable, but the same global variable declared earlier. Hence when we print its value, the output is 101

We changed the value of “f” inside the function. Once the function call is over, the changed value of the variable “f” persists. At line 12, when we again, print the value of “f” is it displays the value “changing global variable”

Variables in Python

Python 2 Example

f = 101;
print f
# Global vs.local variables in functions
def someFunction():
  global f
  print f
  f = "changing global variable"
someFunction()
print f

Python 3 Example

f = 101;
print(f)
# Global vs.local variables in functions
def someFunction():
  global f
  print(f)
  f = "changing global variable"
someFunction()
print(f)

Delete a variable

You can also delete Python variables using the command del “variable name”.

In the below example of Python delete variable, we deleted variable f, and when we proceed to print it, we get error “variable name is not defined” which means you have deleted the variable.

Variables in Python

Example of Python delete variable or Python clear variable :

f = 11;
print(f)
del f
print(f)

Summary:

Variables are referred to “envelop” or “buckets” where information can be maintained and referenced. Like any other programming language Python also uses a variable to store the information.
Variables can be declared by any name or even alphabets like a, aa, abc, etc.
Variables can be re-declared even after you have declared them for once
Python constants can be understood as types of variables that hold the value which can not be changed. Usually Python constants are referenced from other files. Python define constant is declared in a new or separate file which contains functions, modules, etc.
Types of variables in Python or Python variable types : Local & Global
Declare local variable when you want to use it for current function
Declare Global variable when you want to use the same variable for rest of the program

To delete a variable, it uses keyword “del”.

A Beginner’s Guide To Python Variables

A variable is a fundamental concept in any programming language. It is a reserved memory location that stores and manipulates data. This tutorial on Python variables will help you learn more about what they are, the different data types of variables, the rules for naming variables in Python. You will also perform some basic operations on numbers and strings. We’ll use Jupyter Notebook to implement the Python codes.

Variables are entities of a program that holds a value. Here is an example of a variable:

x=100

In the below diagram, the box holds a value of 100 and is named as x. Therefore, the variable is x, and the data it holds is the value.

xvariable

The data type for a variable is the type of data it holds.

In the above example, x is holding 100, which is a number, and the data type of x is a number.

In Python, there are three types of numbers: Integer, Float, and Complex.

Integers are numbers without decimal points. Floats are numbers with decimal points. Complex numbers have real parts and imaginary parts.

Another data type that is very different from a number is called a string, which is a collection of characters.

Let’s see a variable with an integer data type:

x=100

To check the data type of x, use the type() function:

type(x)

type-x

Python allows you to assign variables while performing arithmetic operations.

x=654*6734
type(x)

x-int

To display the output of the variable, use the print() function.

print(x) #It gives the product of the two numbers

Now, let’s see an example of a floating-point number:

x=3.14
print(x)

type(x) #Here the type the variable is float

float

Strings are declared within a single or double quote.

x=’Simplilearn’

print(x)

x=” Simplilearn.”

print(x)

type(x)

In all of the examples above, we only assigned a single value to the variables. Python has specific data types or objects that hold a collection of values, too. A Python List is one such example.

Here is an example of a list:

x=[14,67,9]

print(x)

type(x)

You can extract the values from the list using the index position method. In lists, the first element index position starts at zero, the second element at one, the third element at two, and so on.

To extract the first element from the list x:

print(x[0])

print-x

To extract the third element from the list x:

print(x[2])

Lists are mutable objects, which means you can change the values in a list once they are declared.

x[2]=70 #Reassigning the third element in the list to 70

print(x)

Earlier, the elements in the list had [14, 67, 9]. Now, they have [14, 67, 70].

Tuples are a type of Python object that holds a collection of value, which is ordered and immutable. Unlike a list that uses a square bracket, tuples use parentheses.

x=(4,8,6)

print(x)

type(x)

Similar to lists, tuples can also be extracted with the index position method.

print(x[1]) #Give the element present at index 1, i.e. 8

If you want to change any value in a tuple, it will throw an error. Once you have stored the values in a variable for a tuple, it remains the same.

tuple

When we deal with files, we need a variable that points to it, called file pointers. The advantage of having file pointers is that when you need to perform various operations on a file, instead of providing the file’s entire path location or name every time, you can assign it to a particular variable and use that instead.

Here is how you can assign a variable to a file:

x=open(‘C:/Users/Simplilearn/Downloads/JupyterNotebook.ipynb’,’r’) 

type(x)

Suppose you want to assign values to multiple variables. Instead of having multiple lines of code for each variable, you can assign it in a single line of code.

(x, y, z)=5, 10, 5

xyyz

The following line code results in an error because the number of values assigned doesn’t match with the number of variables declared.

value-error

If you want to assign the same value to multiple variables, use the following syntax:

x=y=z=1

xyz-1

Now, let's look at the various rules for naming a variable.

1. A variable name must begin with a letter of the alphabet or an underscore(_)

Example:

abc=100 #valid syntax

    _abc=100 #valid syntax

    3a=10 #invalid syntax

    @abc=10 #invalid syntax

. The first character can be followed by letters, numbers or underscores.

Example:

a100=100 #valid

    _a984_=100 #valid

    a9967$=100 #invalid

    xyz-2=100 #invalid

Python variable names are case sensitive.

Example:

a100 is different from A100.

    a100=100

  A100=200

Reserved words cannot be used as variable names.

Example:

break, class, try, continue, while, if

break=10

class=5

try=100

Python is more effective and more comfortable to perform when you use arithmetic operations.

The following is an example of adding the values of two variables and storing them in a third variable:

x=20

y=10

result=x+y

print(result)

Similarly, we can perform subtraction as well.

result=x-y

print(result)

result-x-y

Additionally, to perform multiplication and division, try the following lines of code:

result=x*y

print(result)

result=x/y

print(result)

result-print-result

As you can see, in the case of division, the result is not an integer, but a float value. To get the result of the division in integers, use “//” — the integer division.

The division of two numbers gives you the quotient. To get the remainder, use the modulo (%) operator.

modulo

Now that we know how to perform arithmetic operations on numbers let us look at some operations that can be performed on string variables.

var = ‘Simplilearn’

You can extract each character from the variable using the index position. Similar to lists and tuples, the first element position starts at index zero, the second element index at one, and so on.

print(var[0]) #Gives the character at index 0, i.e. S

print(var[4]) #Gives the character at index 4, i.e. l

var-simplilearn

If you want to extract a range of characters from the string variable, you can use a colon (:) and provide the range between the ones you want to receive values from. The last index is always excluded. Therefore, you should always provide one plus the number of characters you want to fetch.

print(var[0:3]) #This will extract the first three characters from zero, first, and second index.

The same operation can be performed by excluding the starting index.

print(var[:3])

print-sim

The following example prints the values from the fifth location until the end of the string.

print-ilearn

Let’s see what happens when you try to print the following:

print(var[0:20]) #Prints the entire string, although the string does not have 20 characters.

var-simplilearn-print

To print the length of a string, use the len() function.

len(var)

len-var

Let’s see how you can extract characters from two strings and generate a new string.

var1 = “It’s Sunday”

var2 = “Have a great day”

The new string should say, “It’s a great Sunday” and be stored in var3.

var3 = var1[:5] + var2[5:13] + var1[5:]

print(var3)

great-sunday

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Conclusion

I hope this blog helped you learn the concepts of Python variables. After reading this blog, you may have learned more about what a variable is, rules for declaring a variable, how to perform arithmetic operations on variables, and how to extract elements from numeric and string variables using the index position.

#python #programming