No tienes acceso a esta clase

¡Continúa aprendiendo! Únete y comienza a potenciar tu carrera

Diagrama de flujo

3/17
Recursos

Aportes 94

Preguntas 1

Ordenar por:

¿Quieres ver más aportes, preguntas y respuestas de la comunidad?

Algo sencillo. 😃

He sacado este ejemplo en este link para poder entenderlo. El ejercicio dice así:
.
"Un año (A) es bisiesto si es múltiplo de 4, exceptuando los múltiplos de 100, que sólo son bisiestos cuando son múltiplos además de 400, por ejemplo el año 1900 no fue bisiesto, pero el año 2000 si lo será. Hacer un organigrama que dado un año A nos diga si es o no bisiesto.
.
A MOD 4 = 0 quiere decir que, si el año es múltiplo de 4 (4, 8, 12, 16…), al dividirse entre 4 debe dar como residuo 0. Pasa al siguiente filtro de ser clasificado como bisiesto (sin serlo aún).
.
A MOD 100 = 0 corresponde a los múltiplos de 100 (100, 200, 300…), al dividirse entre 100 debe dar como residuo 0. Pasa al siguiente filtro de ser clasificado como bisiesto (sin serlo aún).
.
A MOD 400 = 0 corresponde a los múltiplos de 400 (400, 800, 1200…), al dividirse entre 400 debe dar como residuo 0. Es aquí donde ya obtendremos los años que son bisiestos, terminando el ejercicio.
.

Diagrama de flujo

Para mi ejemplo quise representar el proceso cuando vamos a retirar dinero de un cajero automático.

Encontré el mismo ejemplo de la profesora Ana, explicado de distinta manera y logré captarlo mucho más.

Crear un diagrama de flujo de procesos en el que se almacenen 3 números en 3 variables A, B y C. El diagrama debe decidir cual es el mayor y cual es el menor

Un diagrama de flujo es un diagrama que describe un proceso, sistema o algoritmo informático.

Se usan ampliamente en numerosos campos para documentar, estudiar, planificar, mejorar y comunicar procesos que suelen ser complejos en diagramas claros y fáciles de comprender. Los diagramas de flujo emplean rectángulos, óvalos, diamantes y otras numerosas figuras para definir el tipo de paso, junto con flechas conectoras que establecen el flujo y la secuencia.

Pueden variar desde diagramas simples y dibujados a mano hasta diagramas exhaustivos creados por computadora que describen múltiples pasos y rutas.

Si tomamos en cuenta todas las diversas figuras de los diagramas de flujo, son uno de los diagramas más comunes del mundo, usados por personas con y sin conocimiento técnico en una variedad de campos.

Los diagramas de flujo a veces se denominan con nombres más especializados, como “diagrama de flujo de procesos", “mapa de procesos”, “diagrama de flujo funcional”, “mapa de procesos de negocios”, “notación y modelado de procesos de negocio (BPMN)” o
"diagrama de flujo de procesos (PFD)". Están relacionados con otros diagramas populares, como los diagramas de flujo de datos (DFD) y los diagramas de actividad de lenguaje unificado de modelado (UML).

Mi aporte:

![](

Mi aporte, sencillito.

Primer intento gg

En programación existen muchas formas de llegar al mismo resultado, pero planificar cómo lo realizarás puede ayudarte a lograrlo de forma más eficiente, ahí entran en juego los diagramas de flujo.
.
Para el ejercicio propuesto por la docente. Hacer una suma desde 0 a N, propongo esta solución donde el usuario ingresa un número N

.
Los gráficos están hechos con Google Draw (parte de Google Drive). Los símbolos tienen significados según esta lista

Pensar que hago este proceso cada vez que quiero comprar entradas de Cine. Estoy tieso

Diagrama de flujo de un GPS

Diagrama de flujo para usar tu laptop

Cordial saludo comunidad,

Comparto un flujo que hice en MIRO un espacio online, bastante útil para elaborar los diagramas de flujo que necesitemos, espero les sirva la herramienta,

saludos.

https://miro.com

Hola, he creado un ejemplo sencillo para determinar si el número “A” es para o impar.

Saludos,

Este es un proyecto en el que trabajé hace un tiempo. El flujograma señala un análisis sobre las causas de la desnutrición en la comunidad. Se lee de lo más general a lo más específico

Diagrama de flujo para reproducir un DVD

Les dejo el enlace donde encontré más información sobre diagramas de flujo:
https://concepto.de/diagrama-de-flujo/

![](https://static.platzi.com/media/user_upload/diagrama-b1cc7461-7d90-45d0-81c9-c9ab13d3e9bb.jpg)![](https://static.platzi.com/media/user_upload/diagrama-7da11cc9-1195-4f40-86e3-6fb6d81674a1.jpg)
M diagrama ![](https://static.platzi.com/media/user_upload/image-1c70700b-1cdf-48d6-9be3-80ffe28c5c84.jpg)
![](https://static.platzi.com/media/user_upload/image-1e397ff9-e358-4139-9247-44901ba0f62b.jpg)
![](https://static.platzi.com/media/user_upload/Diagrama%20sin%20t%C3%ADtulo-290ea5ae-3938-4117-a678-db0abc30c22a.jpg)

Ejercicio: Sumatoria de Naturales

 

Mi proceso tomando clases en platzi:

Diagrama de flujo de una clase en Platzi

Lo hice a mano alzada utilizando parámetros reales de mi trabajo actual.

Diagrama simple de una búsqueda de cliente en la base de datos.

![](https://static.platzi.com/media/user_upload/Captura%20de%20pantalla%202025-02-08%20a%20la%28s%29%2012.53.35p.m.-29d497f5-ed85-475f-a17c-412b4e9fca28.jpg)
Buen día: Comparto mi diagrama de flujo: ![](https://static.platzi.com/media/user_upload/image-33a175f9-5ffc-4e74-8019-6e39507f6d3e.jpg) Feliz día
![](https://static.platzi.com/media/user_upload/image-2bf4af46-0d90-4283-9dec-6afd99d6c1e7.jpg)
Seleccionador de bebida. ![](https://static.platzi.com/media/user_upload/image-6a7148a1-2975-4205-8f9f-fa922827f741.jpg)
Probando Ejemplo de la clase con DFD: ![](https://static.platzi.com/media/user_upload/image-afeb0300-7663-41a7-9750-a9a81b58757e.jpg)
En el diagrama de flujo de A,B,C deberiamos verificar si A=B o B=C o C=A, Imagina que A=B A>B NO B>C SI ---> B es el mayor estaria dando la respuesta incorrecta.
![]()![](https://static.platzi.com/media/user_upload/DIAGRAMA%20DE%20FLUJO%20PARA%20LA%20ESTAMPACION%20TEXTIL%20DE%20UN%20PRODUCCION-c36d1b0f-c49d-4c1d-8bf2-86b1b35df1bb.jpg)
Aquí dejo mi diagrama, que representa como va mi vida a lo largo de estos meses. ![](https://static.platzi.com/media/user_upload/Diagrama%20de%20Flujo-8fdc95e2-dbe4-4e3e-91ea-cb2a458e19e4.jpg)
![](https://static.platzi.com/media/user_upload/image-97b381b5-be6b-40c2-8d13-d80e69fb93b8.jpg)
![](https://static.platzi.com/media/user_upload/FRITAR%20UN%20HUEVO-eaeb8a96-5d93-436f-a9ce-80b9a6e574d9.jpg)
Mi aporte c:![](https://static.platzi.com/media/user_upload/Diagrama%20de%20flujo%20Platzi%20%281%29_pages-to-jpg-0001-c52b497d-ec9a-483e-b3eb-ee1ef9665409.jpg)
![](<D:\Descargas\Diagrama de flujo Platzi (1)_pages-to-jpg-0001.jpg>)
![](https://static.platzi.com/media/user_upload/image-dfa50f0b-eeb1-4916-a46b-2362bece67a2.jpg)
Hola! Realicé este diagrama en base a una compra de carro o moto. Les agradezco Feedback para saber más. ![](https://static.platzi.com/media/user_upload/image-698007da-cdc4-4026-aa1d-6743e520f80d.jpg)
Mi primer diagrama #proud ![](https://static.platzi.com/media/user_upload/image-266d01b0-fbd2-40e0-af67-e95d0b96f96c.jpg)
Les dejo por acá el mapa de flujo del ejercicio que propuso la profe sobre la suma de valores hasta un valor N: ![]()![](https://static.platzi.com/media/user_upload/Platzi-cdf71618-168a-45ba-b37e-0bc4746270c3.jpg)![]()
![](https://static.platzi.com/media/user_upload/DIAGRMA%20DE%20FLUJO%20EJERCICIO%20-c3d0e641-a61e-4629-9327-cd00a93ead78.jpg)
Dejare esto por aca y me ire lentamente. Paralelogramo: "rectangulo inclinado".
A veces provoca pelear un rato con gente desconocida en redes sociales xd ![](data:image/jpeg;base64,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)

Este video es GENIAL y trae este tema:
https://www.youtube.com/watch?v=u6fusP6JLgg

que buen curso
![](https://static.platzi.com/media/user_upload/image-44b552d6-283c-4da4-95c5-55528bb29caa.jpg)
![]()![](https://static.platzi.com/media/user_upload/DIAGRAMA.F.-e88bbeb3-25cc-45d8-adf6-e8144f4d09f5.jpg)

La idea es como empezar a tener mas claros los procesos de las actividades que vamos hacer con el fin de sacarle el maximo provecho a cada uno y aplicarlo en los macros .
Hice un proceso de creacion de una dashboard, claramente es muy superficial jajaja

Les comparto mi solución de un algoritmo para la suma de números ![]()![](https://static.platzi.com/media/user_upload/Diagrama%20de%20flujo_suma%20de%20N-2dd0baf0-1d7b-44d9-84ea-3c0147931e02.jpg)

MI diagrama de flujo

Diagrama de flujo de comparar y sumar.

Buenas remito mi diagrama realizado.

Es importante los diagramas de flujo ya que con estos podemos ordenar los pasos a seguir y tener un orden en los proceso

Los diagramas de flujo son representaciones visuales esenciales para planear algoritmos y procesos de resolución de problemas antes de programar en Visual Basic. Utilizan formas como círculos, rectángulos y rombos, junto con flechas, para representar inicio, datos, procesos, decisiones y resultados. Estos diagramas guían la secuencia lógica y aseguran una comunicación clara entre el diseño y la implementación.

![](

Me gustaría su feedback, el ejercicio de la profesora emplea bucles pero no sé como representarlos en diagrama de flujo XD, algo así creería que sería el ejercicio de la suma de N números en un diagrama.

Ejercicio de calcular la suma de los N primeros números naturales

Increible

Un ejemplo más personal, compra de un buzo deportivo:

Hola, usé Lucidchart para realizar un ejemplo, es genial, online, se pueden compartir via link, la versión gratuita es muy completa, tiene diferentes templates (diagrama de flujos, organigramas, procesos, etc).
Ejercicio: Hacer un diagrama de flujo que permita leer 2 números diferentes y nos diga cual es el mayor de los 2 números.
link diagrama: https://lucid.app/lucidchart/43c1b598-d022-41a9-ac03-58d61274168f/edit?viewport_loc=-63%2C-25%2C1425%2C1151%2C0_0&invitationId=inv_50045b19-5d3d-41f4-bf03-d453b324743f
imagen:

Les dejo el mío. Algo simple pero efectivo.
También les dejo este Link donde pueden crear diagramas de flujo.
Y es gratis muy fácil de usar la verdad.
🌟🌟🌟🌟Miro 🌟🌟🌟🌟

Buena clase

Hola a todos, comparto un ejemplo sencillo:

A primera vista parece un poco complicado, pero al leer un poco es realmente sencillo de entender, ahora sería ponerlo en práctica para dominarlo, que bueno que tengan un curso especializado.

Un diagrama de flujo de como sumar desde un numero inicio hasta el de finalización !! A color 😄

Comparto mi diagrama:

¿Qué es un diagrama de flujo?


me di cuenta que no tengo herramientas para hacer un diagrama rapido

Aqui el ejemplo solicitado,s e puede realizar con varios software uno de ellos es PSeint
Escribiendo sintaxis del Algoritmo

Diagrama de Flujo Generado por el Software

Ejecución del Algoritmo

Código Ejemplo en Python:

Initialize sum to 0

sum = 0

Initialize loop variable i to 0

i = 0

Loop until i is greater than n

while i <= n:

Add i to sum

sum += i

Increment i by 1

i += 1

The loop has completed, and sum is the sum of the numbers from 0 to n

print(sum)

Herramienta online para hacer diagramas: https://app.diagrams.net/

  • Aprendiendo Diagramas de Flujo (Macros)

Diagrama de flujo es como el maquetado de tu proyecto de macros

En este caso hare una serie sencilla de Fibonacci para mi ejemplo de diagrama de flujo.

Se quiere realizar una serie de Fibonacci donde se calculara la serie 11.
n = 11
se tiene 2 variables donde a es cero y b va ser 1
suma = a + b es 1
pasa por la condicional 1 < 11 si es mayor
entonces a = 1 ya que b es 1 y b seria la suma que es 1
nuevamente pasa por la actividad de suma y a que es 1 suma con b que es 1 donde la suma es 2
luego pasa por la condicional donde  2 < 11 si es mayor
ahora a es igual a 1 y b es la suma a mas b que es 2 
luego pasa por la actividad donde la suma es 3
luego pasa por la condicional donde 3<11 en este caso si
ahora a es igual 2 y b seria la suma de a mas b que es 3
luego pasa por la actividad donde la suma es 5
pasa por la condicional que es 5<11 si
ahora a es igual a 3 y b es la suma de a mas b que es 5
ahora pasa por la actividad de la suma donde es 5 + 3 = 8
pasa por la condicional que es 8<11 si
ahora a es igual 5 y b es la suma de a mas b que es 8
luego pasa por la actividad de la suma donde es 8 + 5 = 13
para por la condicional donde es 13<11 no es mayor
luego termina el diagrama.

Entonces la serie de Fibonacci es: 1,1,2,3,5,8.

![](https://static.platzi.com/media/user_upload/image-db939f54-94bd-45e3-ab1a-52c750b3ae67.jpg)Algo muy sencillo también
![](https://static.platzi.com/media/user_upload/image-eaf33060-3b2d-4f39-816c-827ff0b84552.jpg)
Trabajo en un Almacen![](https://www.mediafire.com/view/5o186fk8ts0ua6i/Captura_de_pantalla_2024-02-10_234013.png/file) aqui dejo mi diagrama de flujo
![](https://www.mediafire.com/view/5o186fk8ts0ua6i/Captura_de_pantalla_2024-02-10_234013.png/file)