Diego  Elizondo

Diego Elizondo


Una Forma Superrápida De Hacer Bucles En Python

Python es conocido por ser un lenguaje de programación lento. Aunque es un hecho que Python es más lento que otros lenguajes, existen algunas formas de acelerar nuestro código Python.

¿Cómo? Simple, optimiza tu código.

Si escribimos código que consume poca memoria y almacenamiento, no solo haremos el trabajo, sino que también haremos que nuestro código Python se ejecute más rápido.

Aquí hay una forma rápida y súper rápida de hacer un bucle en Python que aprendí en uno de los cursos de Python que tomé (¡nunca dejamos de aprender!).

El ciclo promedio

Digamos que queremos sumar los números del 1 al 100000000 (es posible que nunca lo hagamos, pero ese gran número me ayudará a expresar mi punto).

Un enfoque típico sería crear una variable total_sum=0, recorrer un rango e incrementar el valor de total_sumby ien cada iteración.

import time
start = time.time()

total_sum = 0
for i in range(100000000):
    total_sum += i

print(f'Sum: {total_sum}')
print(f'For loop: {time.time() - start} seconds')

Esto hace el trabajo, pero toma alrededor de 6,58 segundos.

Aunque ahora no parece tan lento, se volverá más lento a medida que agregue más 0 al número dentro del rango.

¡Aceleremos esto!

Una forma más rápida de hacer loops usando funciones integradas

Una forma más rápida de hacer un bucle en Python es usar funciones integradas.

En nuestro ejemplo, podríamos reemplazar el bucle for con la sumfunción. Esta función sumará los valores dentro del rango de números.

import time
start = time.time()

total_sum = sum(range(100000000))

print(f'Sum: {total_sum}')
print(f'Sum/range: {time.time() - start} seconds')

El código anterior tarda 0,84 segundos. ¡Es mucho más rápido que el bucle anterior que usamos! Esta es la razón por la que debemos elegir funciones integradas en lugar de bucles.

Pero todavía hay margen de mejora.

Una forma súper rápida de hacer loops usando Numpy

Hace unas semanas, en un curso de ciencia de datos que tomé , aprendí que una de esas prácticas de ingeniería de software que debo seguir para convertirme en un mejor científico de datos es optimizar mi código.

Podemos optimizar bucles mediante operaciones de vectorización. Esto es uno o dos órdenes de magnitud más rápido que sus equivalentes puros de Python (especialmente en cálculos numéricos).

La vectorización es algo que podemos conseguir con NumPy. Numpy es una biblioteca con estructuras de datos eficientes diseñadas para contener datos de matriz. Está escrito principalmente en C, por lo que la velocidad es algo con lo que puede contar.

Intentemos usar los métodos Numpy .sumy .arangeen lugar de las funciones de Python.

import time
import numpy as np
start = time.time()
total_sum = np.sum(np.arange(100000000))

print(f'Sum: {total_sum}')
print(f'Duration: {time.time() - start} seconds')

Esto hace el trabajo en 0,22 segundos. Esto es mucho más rápido que los enfoques anteriores.

Esta es la razón por la que debe usar operaciones vectoriales sobre bucles siempre que sea posible.

Poniendo bucles y Numpy a prueba con más cálculos

Hasta ahora hemos visto una aplicación simple de Numpy, pero ¿qué pasa si no solo tenemos un bucle for, sino también una condición if y más cálculos que hacer?

Aquí es cuando Numpy supera claramente a los bucles.

Imagine que tenemos una serie de puntajes de exámenes aleatorios (del 1 al 100) y queremos obtener el puntaje promedio de aquellos que reprobaron el examen (puntaje <70).

Así es como haríamos esto con un bucle for.

import time
import numpy as np

random_scores = np.random.randint(1, 100, size=100000010)

# SOL1: solving problem using a for loop
start = time.time()

count_failed = 0
sum_failed = 0
for score in random_scores:
    if score < 70:
        sum_failed += score
        count_failed += 1

print(f'For Loop: {time.time() - start} seconds')

Eso toma aproximadamente 15.7 segundos. No está mal, pero podemos obtener resultados más rápidos con Numpy.

Así es como haríamos esto con Numpy.

import time
import numpy as np

random_scores = np.random.randint(1, 100, size=100000010)

# SOL2: solving problem using vector operations
start = time.time()

mean_failed = (random_scores[random_scores < 70]).mean()

print(f'Numpy: {time.time() - start} seconds')

El código anterior tarda unos 0,78 segundos. ¡Eso es mucho más rápido y el código es sencillo!

Fuente: https://towardsdatascience.com/a-super-fast-way-to-loop-in-python-6e58ba377a00


What is GEEK

Buddha Community

Una Forma Superrápida De Hacer Bucles En Python
Shardul Bhatt

Shardul Bhatt


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.


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

Art  Lind

Art Lind


Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development

Art  Lind

Art Lind


How to Remove all Duplicate Files on your Drive via Python

Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.


In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.

Heres a solution

Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.

But How do we do it?

If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?

The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.

There’s a variety of hashing algorithms out there such as

  • md5
  • sha1
  • sha224, sha256, sha384 and sha512

#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips

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

Arvel  Parker

Arvel Parker


Basic Data Types in Python | Python Web Development For Beginners

At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Table of Contents  hide

I Mutable objects

II Immutable objects

III Built-in data types in Python

Mutable objects

The Size and declared value and its sequence of the object can able to be modified called mutable objects.

Mutable Data Types are list, dict, set, byte array

Immutable objects

The Size and declared value and its sequence of the object can able to be modified.

Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.

id() and type() is used to know the Identity and data type of the object







Built-in data types in Python

a**=str(“Hello python world”)****#str**














Numbers (int,Float,Complex)

Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.

#signed interger




Python supports 3 types of numeric data.

int (signed integers like 20, 2, 225, etc.)

float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)

complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)

A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).


The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.

# String Handling

‘Hello Python’

#single (') Quoted String

“Hello Python”

# Double (") Quoted String

“”“Hello Python”“”

‘’‘Hello Python’‘’

# triple (‘’') (“”") Quoted String

In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.

The operator “+” is used to concatenate strings and “*” is used to repeat the string.


output**:****‘Hello python’**

"python "*****2

'Output : Python python ’

#python web development #data types in python #list of all python data types #python data types #python datatypes #python types #python variable type