Tag: tutorial

¿Cómo se puede filtrar una lista en Python utilizando una condición arbitraria? La forma más pitónica y más eficiente es utilizar la comprensión de lista [x for x in list if condition] para filtrar todos los elementos de una lista.

Filtrar con comprensión de lista

La forma más pitónica de filtrar una lista, en mi opinión, es la declaración de comprensión de lista [x for x in list if condition]. Puedes sustituir la condición por cualquier función de x que quieras utilizar como condición de filtrado.

Por ejemplo, si quieres filtrar todos los elementos menores de, digamos, 10, utilizarías la declaración de comprensión de lista [x for x in list if x<10] para crear una nueva lista con todos los elementos de la lista menores de 10.

Aquí hay tres ejemplos de filtrado de una lista:

• Obtener elementos menores de ocho: [x for x in lst if x<8].
• Obtener elementos pares: [x for x in lst if x%2==0].
• Obtener elementos impares: [x for x in lst if x%2].

lst = [8, 2, 6, 4, 3, 1] # Filter all elements <8
small = [x for x in lst if x<8]
print(small) # Filter all even elements
even = [x for x in lst if x%2==0]
print(even) # Filter all odd elements
odd = [x for x in lst if x%2]
print(odd)

El resultado es:

# Elements <8
[2, 6, 4, 3, 1] # Even Elements
[8, 2, 6, 4] # Odd Elements
[3, 1]

Esta es la forma más eficiente de filtrar una lista y también la más pitónica. Pero si buscas alternativas, sigue leyendo porque te explicaré todos y cada uno de los matices del filtrado de listas en Python en esta completa guía.

Filtrar una lista en Python con filter()

La función filter(function, iterable) toma como entrada una función que recibe un argumento (un elemento de lista) y devuelve un valor booleano sobre si este elemento de lista debe pasar el filtro. Todos los elementos que pasan el filtro se devuelven como un nuevo objeto iterable (un objeto filter).

Puedes emplear una declaración de función lambda para crear la función justo donde la pasas como argumento. La sintaxis de la función lambda es lambda x: expression y significa que utilizas x como argumento de entrada y devuelves expression como resultado (que puede o no utilizar x para decidir sobre el valor de retorno). Para más información, consulta mi artículo detallado del blog sobre la función lambda.

lst = [8, 2, 6, 4, 3, 1] # Filter all elements <8
small = filter(lambda x: x<8, lst)
print(list(small)) # Filter all even elements
even = filter(lambda x: x%2==0, lst)
print(list(even)) # Filter all odd elements
odd = filter(lambda x: x%2, lst)
print(list(odd))

El resultado es:

# Elements <8
[2, 6, 4, 3, 1] # Even Elements
[8, 2, 6, 4] # Odd Elements
[3, 1]

La función filter() devuelve un objeto filter que es iterable. Para convertirlo en una lista, se utiliza el constructor list(...).

Artículo relacionado:

• Cómo filtrar usando la función lambda

Filtrar una lista en Python con map()

Sólo añado esta opción porque hay gente que aún intenta utilizar la función map() para filtrar elementos de una lista. Está claro que esta es la forma incorrecta de hacerlo. La razón es que la función map() sólo le permite transformar cada elemento de una lista en un nuevo elemento. Pero seguirás teniendo el mismo número de elementos en la lista. Por lo tanto, necesitas un paso adicional para filtrar todos los elementos (por ejemplo, utilizando comprensión de lista). Pero si estás dispuesto a dar este paso adicional, también podrías haber utilizado una comprensión de lista para filtrar desde un principio.

A esto me refiero:

lst = [8, 2, 6, 4, 3, 1] # Filter all elements <8
small = list(map(lambda x: x if x<8 else None, lst))
small = [x for x in small if x!=None]
print(small) # Filter all even elements
even = list(map(lambda x: x if x%2==0 else None, lst))
even = [x for x in even if x!=None]
print(even) # Filter all odd elements
odd = list(map(lambda x: x if x%2 else None, lst))
odd = [x for x in odd if x!=None]
print(odd)

El resultado vuelve a ser el mismo:

[2, 6, 4, 3, 1]
[8, 2, 6, 4]
[3, 1]

Pero el método para obtener este resultado es claramente ineficaz y no resulta legible.

Artículos Relacionados:

• Cómo deshacerse de la función map() (y por qué Guido la odiaba)

Filtrar una lista en Python con generador

Una expresión generadora crea un iterador sobre una secuencia de valores. Funciona igual que la comprensión de listas, pero sin crear un tipo de datos de lista. Esto es un poco más eficiente.

Puedes utilizar expresiones generadoras en cualquier llamada a una función que requiera un iterable como entrada. Por ejemplo, si quieres calcular la suma de todos los valores que cumplen una determinada condición en una lista.

• Primero, determina el iterable de valores que cumplen una determinada condición.
• En segundo lugar, suma todos esos valores con la función sum().

Aquí tienes un ejemplo de código que muestra cómo sumar todos los valores enteros de una lista (e ignorar el resto) utilizando una expresión generadora para filtrar los no enteros:

lst = [6, 8, 2, 8, 'Alice']
print(sum(x for x in lst if type(x) == int))
# 24

Comprueba el type(x) de cada elemento y compáralo con el tipo integer. Esta comparación devuelve True si el elemento es, de hecho, de tipo entero.

Filtrar una lista en Python con condición

Puedes definir cualquier condición compleja sobre un elemento de la lista para decidir si se filtra o no. Sólo tienes que crear tu propia función (por ejemplo, condition(x)) que tome un elemento de la lista como entrada y devuelva el valor booleano True si se cumple la condición o False en caso contrario.

Aquí tienes un ejemplo de código:

def condition(x): '''Define your arbitrarily complicated condition here''' return x<10 and x>0 lst = [11, 14, 3, 0, -1, -3] # Filter out all elements that do
# not meet condition
filtered = [x for x in lst if condition(x)]
print(filtered)
# [3]

Todos los elementos que son menores que 10 y mayores que 0 se incluyen en la lista filtrada. Por lo tanto, solo queda el elemento 3.

Filtrar una lista en Python con condiciones múltiples

Lo mismo se aplica si quieres combinar varias condiciones. Digamos que deseas filtrar todos los elementos x>9 y x<1. Estas son dos condiciones (simples). Puedes definir cualquier condición compleja sobre un elemento de la lista para decidir si se filtra o no. Sólo tienes que crear tu propia función (por ejemplo, condition(x)) que tome un elemento de la lista como entrada y devuelva el valor booleano True si se cumple la condición o False en caso contrario.

Aquí está el mismo ejemplo de código que antes:

def condition(x): '''Define your arbitrarily complicated condition here''' return x<10 and x>0 lst = [11, 14, 3, 0, -1, -3] # Filter out all elements that do
# not meet condition
filtered = [x for x in lst if condition(x)]
print(filtered)
# [3]

Todos los elementos que son menores que 10 y mayores que 0 se incluyen en la lista filtrada. Por lo tanto, solo queda el elemento 3.

Filtrar una lista en Python con Regex

Problema: Dada una lista de cadenas. ¿Cómo puedes filtrar aquellas que coincidan con una determinada expresión regular?

Ejemplo: Digamos que tienes una lista ['Alice', 'Anne', 'Ann', 'Tom'] y quieres descartar los elementos que no cumplen el patrón regex 'A.*e'. Esperas que la lista filtrada sea ['Alice', 'Anne'].

Solución: Utiliza el marco de filtrado de comprensión de lista [x for x in list if match] para filtrar todos los elementos que no concuerden con una cadena determinada.

import re # Define the list and the regex pattern to match
customers = ['Alice', 'Anne', 'Ann', 'Tom']
pattern = 'A.*e' # Filter out all elements that match the pattern
filtered = [x for x in customers if re.match(pattern, x)] print(filtered)
# ['Alice', 'Anne']

Utiliza el método re.match() que devuelve un objeto match si hay una coincidencia o None en caso contrario. En Python, cualquier objeto match se evalúa como True (si es necesario), salvo algunas excepciones (por ejemplo, None, 0, 0.0, etc.). Si necesitas refrescar tus conocimientos básicos sobre la función re.match(), echa un vistazo a mi detallado artículo del blog que te lleva paso a paso por esta potente herramienta de Python.

Artículos Relacionados:

• Cómo contar elementos de una lista que coincidan con una condición
• Cómo hacer coincidir una palabra exacta en Python
• Cómo contar el número de coincidencias en Python

¿Cómo filtrar una lista de listas en Python?

Respuesta corta: Para filtrar una lista de listas por una condición en las listas internas, utiliza la declaración de comprensión de lista [x for x in list if condition(x)] y sustituye condition(x) por tu condición de filtrado que devuelva True para incluir la lista interna x, y False en caso contrario.

Las listas pertenecen a las estructuras de datos más importantes de Python: ¡todo programador experto las conoce a fondo! Sorprendentemente, incluso los programadores intermedios no saben la mejor manera de filtrar una lista, y mucho menos una lista de listas en Python. ¡Este tutorial te muestra cómo hacer esto último!

Problema: Digamos que tienes una lista de listas. Quieres filtrar la lista de listas para que sólo queden las listas interiores que satisfagan una determinada condición. La condición es una función de la lista interna, como la media o la suma de los elementos de la lista interna.

Ejemplo: Dada la siguiente lista de listas con mediciones semanales de temperatura, y una lista interior semanal.

# Measurements of a temperature sensor (7 per week)
temperature = [[10, 8, 9, 12, 13, 7, 8], # week 1 [9, 9, 5, 6, 6, 9, 11], # week 2 [10, 8, 8, 5, 6, 3, 1]] # week 3

¿Cómo filtrar las semanas más frías con un valor medio de temperatura <8? Este es el resultado que deseas:

print(cold_weeks)
# [[9, 9, 5, 6, 6, 9, 11], [10, 8, 8, 5, 6, 3, 1]]

Hay dos métodos semánticamente equivalentes para lograr esto: la comprensión de listas y la función map().

Artículos Relacionados:

• ¿Cómo filtrar una lista de listas en Python?
• Comprensión de listas — Lista de listas en Python
• Filter() vs comprensión de lista
• Comprensión de lista anidada
• La guía definitiva para las listas en Python
• Comprensión de lista

Filtrar una lista de cadenas en Python

Problema: Dada una lista de cadenas y una cadena de búsqueda. ¿Cómo puedes filtrar las que contienen la cadena de búsqueda?

Ejemplo: Digamos que tienes una lista ['Alice', 'Anne', 'Ann', 'Tom'] y quieres obtener todos los elementos que contienen la subcadena 'An'. Esperas que la lista filtrada sea ['Anne', 'Ann'].

Solución: Utiliza el esquema de filtrado de comprensión de lista [x for x in list if condition] para descartar todos los elementos que no contengan otra cadena.

import re # Define the list
customers = ['Alice', 'Anne', 'Ann', 'Tom'] # Filter out all elements that contain 'An'
filtered = [x for x in customers if 'An' in x] print(filtered)
# ['Anne', 'Ann']

Utiliza la operación básica de pertenencia a la cadena “in” para comprobar si un elemento pasa el filtro o no.

Filtrar una lista en Python para eliminar cadenas vacías

Problema: Dada una lista de cadenas. ¿Cómo puedes eliminar todas las cadenas vacías?

Ejemplo: Digamos que tienes una lista ['Alice', 'Anne', '', 'Ann', '', 'Tom'] y quieres obtener una nueva lista ['Alice', 'Anne', 'Ann', 'Tom'] de cadenas no vacías.

Solución: Utiliza el esquema de filtrado de comprensión de lista [x for x in list if x] para filtrar todas las cadenas vacías.

import re # Define the list
customers = ['Alice', 'Anne', '', 'Ann', '', 'Tom'] # Filter out all elements that contain 'An'
filtered = [x for x in customers if x] print(filtered)
# ['Alice', 'Anne', 'Ann', 'Tom']

Aprovechas la característica de que Python asigna el valor booleano False a la cadena vacía ''.

Filtrar una lista en Python con endswith() y startswith()

Problema: Dada una lista de cadenas. ¿Cómo puedes filtrar aquellas que comienzan con otra cadena (o terminan con otra cadena)? En otras palabras, deseas obtener todas las cadenas que tienen otra cadena como prefijo o sufijo.

Ejemplo: Digamos que tienes una lista ['Alice', 'Anne', 'Ann', 'Tom'] y quieres obtener todos los elementos que empiezan por 'An'. Esperas que la lista filtrada sea ['Anne', 'Ann'].

Solución: Utiliza el esquema de filtrado de comprensión de lista [x for x in list if x.startswith('An')] para filtrar todos los elementos que empiezan por 'An'. Si deseas comprobar cadenas que terminan con otra cadena, puedes usar la función str.endswith() en su lugar.

import re # Define the list
customers = ['Alice', 'Anne', 'Ann', 'Tom'] # Filter out all elements that start with 'An'
filtered = [x for x in customers if x.startswith('An')] print(filtered)
# ['Anne', 'Ann'] # Filter out all elements that end with 'e'
filtered = [x for x in customers if x.endswith('e')] print(filtered)
# ['Alice', 'Anne']

Utiliza las funciones startswith() y endswith() como condiciones de filtrado.

Filtrar una lista en Python con lambda

La función filter(function, iterable) toma como argumento una función de filtrado que toma un elemento de la lista como entrada y devuelve el valor booleano True si se cumple la condición o False en caso contrario. Esta función decide si un elemento se incluye en la lista filtrada o no.

Para definir esta función, puedes utilizar la palabra clave lambda. La función lambda es una función anónima: piensa en ella como una función desechable que sólo se necesita como argumento y para nada más en el código.

Este es el código que muestra cómo utilizar la función lambda para filtrar una lista y devolver sólo los valores impares de la lista:

# Create the list
lst = [1, 2, 3, 4] # Get all odd values
print(list(filter(lambda x: x%2, lst)))
# [1, 3]

La función lambda x: x%2 toma un argumento x -el elemento que hay que comprobar con el filtro- y devuelve el resultado de la expresión x%2. Esta expresión de módulo devuelve 1 si el entero es impar y 0 si es par. Por lo tanto, todos los elementos impares pasan la prueba.

Filtrar una lista en Python con otra lista

Problema: Dada una lista de valores lst y una lista de booleanos filter. ¿Cómo filtrar la primera lista usando la segunda lista? Más específicamente, quieres crear una nueva lista que incluya el i-ésimo elemento de lst si el i-ésimo elemento de filter es True.

Ejemplo: Aquí hay dos listas de ejemplo:

lst = [1, 2, 3, 4]
filter_lst = [True, False, False, True]

Y deseas obtener esta lista:

[1, 4]

Solución: Utiliza una simple declaración de comprensión de lista [lst[i] for i in range(len(lst)) if filter_lst[i]] que compruebe para cada índice i si el valor booleano del filtro correspondiente es True. En este caso, se añade el elemento en el índice i en lst a la nueva lista filtrada. Este es el código:

lst = [1, 2, 3, 4]
filter_lst = [True, False, False, True] res = [lst[i] for i in range(len(lst)) if filter_lst[i]]
print(res)
# [1, 4]

La lista booleana sirve de “máscara” que determina qué elemento pasa el filtro y cuál no.

Una alternativa es usar la función zip() para iterar sobre múltiples secuencias sin necesidad de tocar ningún índice:

lst = [1, 2, 3, 4]
filter_lst = [True, False, False, True] res = [x for (x, boo) in zip(lst, filter_lst) if boo]
print(res)
# [1, 4]

¿Necesitas mejorar tu comprensión de zip()? ¡Echa un vistazo a nuestro exhaustivo artículo del blog!

Filtrar una lista en Python con índices

Problema: dada una lista de valores y una lista de índices. ¿Cómo filtrar todos los elementos con índices en la segunda lista?

Ejemplo: Tienes la lista ['Alice', 'Bob', 'Ann', 'Frank'] y los índices [1, 2]. Lo que estás buscando es la lista filtrada ['Bob', 'Ann'].

Solución: Recorre todos los índices de la segunda lista e incluye los elementos correspondientes de la lista mediante una simple sentencia de comprensión de lista [lst[i] for i in indices].

lst = ['Alice', 'Bob', 'Ann', 'Frank']
indices = [1, 2] res = [lst[i] for i in indices]
print(res)
# ['Bob', 'Ann']

Solo dos elementos con índices 1 y 2 pasan el filtro.

Filtrar en Python una lista de diccionarios

Problema: Dada una lista de diccionarios. Cada diccionario consta de uno o más pares (clave, valor). Quieres filtrarlos por el valor de una determinada clave del diccionario (atributo). ¿Cómo puedes lograrlo?

Ejemplo minimalista: Considera el siguiente ejemplo en el que tienes tres diccionarios de usuarios con las claves username, age y play_time. Quieres obtener una lista de todos los usuarios que cumplen una determinada condición, como play_time > 100. Esto es lo que intentas conseguir:

users = [{'username': 'alice', 'age': 23, 'play_time': 101}, {'username': 'bob', 'age': 31, 'play_time': 88}, {'username': 'ann', 'age': 25, 'play_time': 121},] superplayers = # Filtering Magic Here print(superplayers)

El resultado debería parecerse a esto, donde el atributo play_time determina si un diccionario pasa el filtro o no, es decir, play_time>100:

[{'username': 'alice', 'age': 23, 'play_time': 101},
{'username': 'ann', 'age': 25, 'play_time': 121}]

Solución: Utiliza la comprensión de lista [x for x in lst if condition(x)] para crear una nueva lista de diccionarios que cumplan la condición. Todos los diccionarios en lst que no cumplen con la condición se filtran. Puedes definir tu propia condición sobre el elemento x de la lista.

Aquí tienes el código que te muestra cómo filtrar todos los diccionarios de usuario que no cumplen la condición de haber jugado al menos 100 horas.

users = [{'username': 'alice', 'age': 23, 'play_time': 101}, {'username': 'bob', 'age': 31, 'play_time': 88}, {'username': 'ann', 'age': 25, 'play_time': 121},] superplayers = [user for user in users if user['play_time']>100] print(superplayers)

La salida es la lista filtrada de diccionarios que cumplen la condición:

[{'username': 'alice', 'age': 23, 'play_time': 101},
{'username': 'ann', 'age': 25, 'play_time': 121}]

Artículos relacionados en el blog de Finxter:

• Cómo filtrar una lista de diccionarios
• Comprensión de lista
• Funciones Lambda
• Diccionarios

Filtrar en Python elementos únicos en una lista – eliminar duplicados

¿Cómo eliminar todos los duplicados de un valor dado en la lista?

El enfoque ingenuo es revisar cada elemento y verificar si este elemento ya existe en la lista. Si es así, retíralo. Sin embargo, esto requiere unas cuantas líneas de código.

Una forma más corta y concisa es crear un diccionario a partir de los elementos de la lista. Cada elemento de la lista se convierte en una nueva clave del diccionario. Todos los elementos que aparezcan varias veces se asignarán a la misma clave. El diccionario sólo contiene claves únicas, no puede haber varias claves iguales.

Como valores del diccionario, simplemente tomas valores ficticios (por defecto).

Artículos relacionados del blog:

• ¡Echa un vistazo a mi último tutorial de diccionario para un aprendizaje máximo!

Luego, sólo tienes que volver a convertir el diccionario en una lista, desechando los valores ficticios. Como las claves del diccionario permanecen en el mismo orden, no se pierde la información de orden de los elementos de la lista original.

Este es el código:

>>> lst = [1, 1, 1, 3, 2, 5, 5, 2]
>>> dic = dict.fromkeys(lst)
>>> dic
{1: None, 3: None, 2: None, 5: None}
>>> duplicate_free = list(dic)
>>> duplicate_free
[1, 3, 2, 5]

Filtrar en Python un rango en una lista

Filtrar todos los elementos de una lista que estén dentro del rango de valores entre los índices de start y stop dados.

lst = [3, 10, 3, 2, 5, 1, 11]
start, stop = 2, 9 filtered_lst = [x for x in lst if x>=start and x<=stop]
print(filtered_lst)
# [3, 3, 2, 5]

Empleas la condición x>=start y x<=stop para comprobar si la lista de elementos x está dentro del rango [start, stop] o no.

Filtrar en Python una lista con mayor que y menor que

Filtrar todos los elementos de una lista que sean mayores que un valor determinado y.

lst = [3, 10, 3, 2, 5, 1, 11]
y = 2 filtered_lst = [x for x in lst if x>y]
print(filtered_lst)
# [3, 10, 3, 5, 11]

Utiliza la condición x > y para comprobar si el elemento de lista x es mayor que y o no. En el primer caso, se incluye en la lista filtrada. En el último, no.

Puedes utilizar la misma idea con el operador menor que < mediante la declaración de comprensión de lista [x for x in lst if x<y].

Recuento de listas filtradas en Python

¿Cómo puedes contar elementos bajo una determinada condición en Python? Por ejemplo, ¿qué pasa si quieres contar todos los valores pares de una lista? ¿O todos los números primos? ¿O todas las cadenas que comienzan con un determinado carácter? Hay múltiples formas de conseguirlo, vamos a discutirlas una por una.

Digamos que tienes una condición para cada elemento x. Convirtámosla en una función con el nombre condition(x). Puedes definir cualquier condición que quieras, sólo tienes que ponerla en tu función. Por ejemplo, esta condición devuelve True para todos los elementos que son mayores que el entero 10:

def condition(x): return x > 10 print(condition(10))
# False print(condition(2))
# False print(condition(11))
# True

Pero también puedes definir condiciones más complicadas, como comprobar si son números primos.

Recuento de listas en Python con if

¿Cómo puedes contar los elementos de la lista SI se cumple la condición?

La respuesta es utilizar una simple expresión generadora sum(condition(x) for x in lst):

>>> def condition(x): return x>10 >>> lst = [10, 11, 42, 1, 2, 3]
>>> sum(condition(x) for x in lst)
2

El resultado indica que hay dos elementos que son más grandes que 10. Has utilizado una expresión generadora que devuelve un iterador de booleanos. Ten en cuenta que el booleano True se representa con el valor entero 1 y el booleano False se representa con el valor entero 0. Por eso puedes calcular simplemente la suma de todos los booleanos para obtener el número de elementos para los que se cumple la condición.

Recuento de listas en Python con mayor / menor que

Si quieres determinar el número de elementos que son mayores o menores que un valor especificado, sólo tienes que modificar la condición de este ejemplo:

>>> def condition(x): return x>10 >>> lst = [10, 11, 42, 1, 2, 3]
>>> sum(condition(x) for x in lst)
2

Por ejemplo, para encontrar el número de elementos menores que 5, usa la condición x<5 en la expresión del generador:

>>> lst = [10, 11, 42, 1, 2, 3]
>>> sum(x<5 for x in lst)
3

Recuento de listas en Python de cero / no cero

Para contar el número de ceros en una lista dada, usa la llamada al método list.count(0).

Para contar el número de no-ceros en una lista dada, debes utilizar el recuento condicional, tal y como se comentó anteriormente:

def condition(x): return x!=0 lst = [10, 11, 42, 1, 2, 0, 0, 0]
print(sum(condition(x) for x in lst))
# 5

Recuento de listas en Python con lambda + map

Una alternativa es utilizar una combinación de las funciones map y lambda.

Artículos Relacionados:

• [Tutorial completo] Función map: manipula cada elemento de un iterable.
• [Tutorial completo] Función Lambda: crea una función anónima.

Este es el código:

>>> sum(map(lambda x: x%2==0, [1, 2, 3, 4, 5]))
2

Cuenta el número de enteros pares en la lista.

• La función lambda devuelve un valor booleano para un elemento dado x.
• La función map transforma cada elemento de lista en un valor booleano (1 o 0).
• La función sum suma los “1”.

El resultado es el número de elementos para los que la condición se evalúa como True.

Filtrar una lista por longitud de cadena en Python

Dada una lista de cadenas. ¿Cómo obtener todos los elementos que tienen más de x caracteres? En otras palabras: ¿cómo filtrar una lista por longitud de cadena?

coders = ['Ann', 'Alice', 'Frank', 'Pit']
filtered = [x for x in coders if len(x)>3]
print(filtered)
# ['Alice', 'Frank']

La sentencia de comprensión de lista [x for x in coders if len(x)>3] filtra todas las cadenas que tienen más de tres caracteres.

Filtrar los elementos None de una lista en Python

¿Cómo eliminar todos los valores None de una lista? Por ejemplo, tienes la lista ['Alice', None, 'Ann', None, None, 'Bob'] y quieres la lista ['Alice', 'Ann', 'Bob']. ¿Cómo puedes lograrlo?

coders = ['Alice', None, 'Ann', None, None, 'Bob']
filtered = [x for x in coders if x]
print(filtered)
# ['Alice', 'Ann', 'Bob']

En Python, cada elemento tiene un valor booleano asociado, por lo que puede usar cualquier objeto Python como condición. El valor None está asociado al valor booleano False.

Filtrar en Python una lista JSON

Problema: Digamos que tienes un objeto de lista JSON. Quieres filtrar la lista en función de un atributo. ¿Cómo lograrlo?

Ejemplo: Dada la siguiente lista JSON.

json = [ { "user": "alice", "type": "free" }, { "user": "ann", "type": "paid" }, { "user": "bob", "type": "paid" }
]

Quieres encontrar todos los usuarios que tienen un tipo de cuenta 'paid'.

[ { "user": "ann", "type": "paid" }, { "user": "bob", "type": "paid" }
]

Solución: Utiliza la comprensión de lista [x for x in json if x['type']=='paid'] para filtrar la lista y obtener una nueva lista json con los objetos que pasan el filtro.

json = [ { "user": "alice", "type": "free" }, { "user": "ann", "type": "paid" }, { "user": "bob", "type": "paid" }
] filtered = [x for x in json if x['type']=='paid']
print(filtered)
# [{'user': 'ann', 'type': 'paid'},
# {'user': 'bob', 'type': 'paid'}]

Sólo Ann y Bob tienen una cuenta de pago y pasan el test x['type']=='paid'.

Filtrar en una línea una lista en Python

¿Quieres filtrar tu lista por una condición dada en una línea de código? Utiliza la declaración de comprensión de lista [x for x in list if condition], en la que la parte de la condición puede ser cualquier expresión booleana sobre x. Esta sentencia de una sola línea devuelve un nuevo objeto de lista con todos los elementos que pasan el “test” de filtrado.

Aquí hay un ejemplo:

lst = ['Alice', 3, 5, 'Bob', 10] # ONE-LINER:
f = [x for x in lst if type(x)==str] print(f)
# ['Alice', 'Bob']

La sentencia filtra todos los elementos de la lista y comprueba si son de tipo cadena. Si lo son, pasan la prueba y se incluyen en la nueva lista.

Si te gustan las sentencias de una sola línea, te encantará mi libro Python One-Liner (NoStarch Press 2020). Te muestra exactamente cómo escribir código pitónico y comprimir tu pensamiento y codificación a la forma más minimalista.

Cómo filtrar una lista en Python de forma eficiente: filter() frente a comprensión de lista

[Spoiler] ¿Qué es más rápido para filtrar una lista: filter() o comprensión de lista? Para listas grandes con un millón de elementos, filtrar listas con comprensión de lista es un 40% más rápido que el método incorporado filter().

Para responder a esta pregunta, he escrito un breve script que comprueba el rendimiento en tiempo de ejecución del filtrado de grandes listas de tamaños crecientes mediante los métodos filter() y de comprensión de lista.

Mi tesis es que el método de comprensión de lista debería ser ligeramente más rápido para los tamaños de lista más grandes, porque aprovecha la eficiente implementación de cPython de la comprensión de lista y no necesita llamar a una función adicional.

Utilicé mi portátil con un procesador Intel(R) Core(TM) i7-8565U a 1,8 GHz (con Turbo Boost hasta 4,6 GHz) y 8 GB de RAM.

A continuación, creé 100 listas con ambos métodos con tamaños que iban de 10.000 elementos a 1.000.000 de elementos. Como elementos, simplemente incrementé los números enteros en uno a partir de 0.

Este es el código que he utilizado para medir y representar los resultados: ¿qué método es más rápido: filter() o una comprensión de lista?

import time # Compare runtime of both methods
list_sizes = [i * 10000 for i in range(100)]
filter_runtimes = []
list_comp_runtimes = [] for size in list_sizes: lst = list(range(size)) # Get time stamps time_0 = time.time() list(filter(lambda x: x%2, lst)) time_1 = time.time() [x for x in lst if x%2] time_2 = time.time() # Calculate runtimes filter_runtimes.append((size, time_1 - time_0)) list_comp_runtimes.append((size, time_2 - time_1)) # Plot everything
import matplotlib.pyplot as plt
import numpy as np f_r = np.array(filter_runtimes)
l_r = np.array(list_comp_runtimes) print(filter_runtimes)
print(list_comp_runtimes) plt.plot(f_r[:,0], f_r[:,1], label='filter()')
plt.plot(l_r[:,0], l_r[:,1], label='list comprehension') plt.xlabel('list size')
plt.ylabel('runtime (seconds)') plt.legend()
plt.savefig('filter_list_comp.jpg')
plt.show()

El código compara los tiempos de ejecución de la función filter() y la variante de comprensión de lista para filtrar una lista. Ten en cuenta que la función filter() devuelve un objeto filter, por lo que debes convertirlo en lista mediante el constructor list().

Aquí está el gráfico resultante que compara el tiempo de ejecución de los dos métodos. En el eje x, puedes ver el tamaño de la lista de 0 a 1.000.000 de elementos. En el eje y, puedes ver el tiempo de ejecución que se necesita en segundos para ejecutar las respectivas funciones.

El gráfico resultante muestra que ambos métodos son extremadamente rápidos para unas decenas de miles de elementos. De hecho, son tan rápidos que la función time() del módulo time no puede capturar el tiempo transcurrido.

Pero a medida que aumentas el tamaño de las listas a cientos de miles de elementos, el método de comprensión de lista empieza a ganar:

Para listas grandes con un millón de elementos, filtrar listas con comprensión de lista es un 40% más rápido que el método incorporado filter().

El motivo es la eficiente implementación de la declaración de comprensión de lista. Sin embargo, es interesante la siguiente observación. Si no conviertes la función filter en una lista, obtendrás el siguiente resultado:

De repente, la función filter() tiene un tiempo de ejecución constante cercano a 0 segundos, independientemente del número de elementos que haya en la lista. ¿Por qué está pasando esto?

La explicación es simple: la función filter devuelve un iterador, no una lista. El iterador no necesita calcular un solo elemento hasta que se le pida que calcule el elemento next(). Por lo tanto, la función filter() calcula el siguiente elemento sólo si es necesario hacerlo. Sólo si lo conviertes en lista, tendrá que computar todos los valores. De lo contrario, no calcula de antemano ningún valor.

A dónde ir desde aquí

Este tutorial te ha mostrado los entresijos de la función filter() en Python y la ha comparado con el método de comprensión de lista para filtrar: [x for x in list if condition]. Has podido comprobar que esto último no sólo es más legible y más pitónico, sino también más rápido. ¡Así que adopta el enfoque de comprensión de lista para filtrar listas!

Si te encanta programar y quieres hacerlo a tiempo completo desde la comodidad de tu propia casa, estás de suerte:

He creado un seminario web gratuito que muestra cómo comencé como freelancer de Python después de mis estudios de informática trabajando desde casa (y viendo crecer a mis hijos) mientras ganaba un ingreso a tiempo completo trabajando solo horas a tiempo parcial.

Seminario web: ¿Cómo convertirse en un freelance de Python de seis cifras?

Únete a 21.419 codificadores ambiciosos de Python. Es divertido!

Microsoft Excel is a cross-platform and battle-tested spreadsheet software widely used for data analysis and visualization. It is a powerful and user-friendly tool indeed! But how can we bring it to the next level? 

We can combine Excel with Python to

• read data from Excel,
• save data to Excel with formats, formulas, and even charts, and
• automate Excel tasks in Python!

Please continue reading and stay tuned for my Excel in Python series if it sounds great!

This tutorial is all about saving data to Excel.

Concretely, I will first introduce Excel’s data structure and lingos.

Then, you will learn the difference between four popular Python ways to save data to excel, including pandas and openpyxl.

Finally, I will focus on the pandas.DataFrame.to_excel() method. I will guide you through four actual use cases in pandas.DataFrame.to_excel(), ranging from one Excel worksheet, multiple Excel worksheets, multiple Excel workbooks, and dealing with index cases. 

You can find all datasets and codes in this tutorial here. The data in our example datasets are not actual data and are only used for educational purposes.

Quick Introduction to Excel

Before jumping into reading data from Excel, let’s look at how data is stored in Excel and get ourselves familiar with some Excel lingos.

Simply put, data is stored in cells in Excel, and each cell can be identified with its unique row and column number pair. 

Columns in Excel are labeled in alphabets, starting from ‘A’, and rows in Excel are labeled in roman numbers, starting from ‘1’. For example, in the following Excel picture, A1 is the cell in the intersection of the first column and first row, ‘ID’.

Yes, you got the idea! It is just like the DataFrame data structure in our friends, pandas.

And if you want to select an area to dump data into Excel, you can imagine drawing a rectangle data region based on the upper-left cell of the part.

On top of that, other commonly seen Excel lingos include worksheets and workbooks. 

• A worksheet means a single spreadsheet in an Excel file. 
• A workbook means a single Excel file with extensions like .xlsx and .xls. 

Tip: More file extensions supported by Excel can be found in the Microsoft official doc here.

Now, you are ready to know how to read data from Excel!

Popular Python ways to Save Data to Excel

Here is the summary of popular Python ways to save data to excel:

One of pandas.DataFrame.to_excel()’s advantages is engine compatibility. It uses multiple external engines for different Excel extensions, including openpyxl, xlwt, and xlsxwriter.

• “xlwt” supports old-style Microsoft Excel versions 95 to 2003 files (.xls).
• “openpyxl” supports newer Excel 2010 file formats (.xlsx, .xlsm, .xltx, .xltm).
• “xlsxwriter” supports Excel 2007+ file format (.xlsx).

In summary, you can use openpyxl and other libraries for specific Excel file formats and basic data processing. And please remember that openpyxl and xlsxwriter support newer Excel file formats. 

However, I recommend using pandas.DataFrame.to_excel() for data science and analytics applications because it supports most Excel file formats and accepts a DataFrame object with powerful methods.

Therefore, let’s see how to use pandas.DataFrame.to_excel() to save data to Excel!

Saving Data to Excel – pandas.DataFrame.to_excel()

Since pandas.DataFrame.to_excel() is the most powerful and inclusive way to save data from Excel files with different extensions, I will first introduce its syntax and walk you through three use cases with real Python codes below using pandas.DataFrame.to_excel().

Meet pandas.ExcelWriter

Before heading over to the pandas.DataFrame.to_excel method, we need to know a new friend, pandas.ExcelWriter. It is a class for writing pandas.DataFrame objects into excel sheets.

When you are trying to write to multiple sheets, you need to create an ExcelWriter object and pass it to pandas.DataFrame.to_excel() as the first parameter.

To create an ExcelWriter object, we pass something to it according to its syntax:

# Syntax for pandas.ExcelWriter
pandas.ExcelWriter(path[, engine=None[, date_format=None[, datetime_format=None[, mode='w'[, storage_options=None[, if_sheet_exists=None[, engine_kwargs=None[, **kwargs]]]]]])

Parameter	Meta	Description
path	required	String or typing.BinaryIO to an Excel file.
engine	optional	String specifying the engine to use for writing. Default is to use :  xlwt for xls  xlsxwriter for xlsx if xlsxwriter is installed otherwise openpyxl odf for ods
date_format	optional	Format string for dates written into Excel files (e.g. 'YYYY-MM-DD').
datetime_format	optional	Format string for datetime objects written into Excel files. (e.g. 'YYYY-MM-DD HH:MM:SS').
mode	optional	File mode to use ('w' for write; 'a' for append). The default is to use 'w'.
storage_options	optional	A dictionary that makes sense for a particular storage connection, e.g. host, port, username, password, etc.
if_sheet_exists	optional	What to do if a sheet exists under the append mode. Accepts {'error', 'new', 'replace', 'overlay'}. Default is 'error'. error: raise a ValueError. new: create a new sheet, with a name determined by the engine. replace: delete the contents of the sheet before writing to it. overlay: write contents to the existing sheet without removing the old contents.
engine_kwargs	optional	A dictionary containing keyword arguments passed into the engine.

Tip: Generally, you only need to use the four parameters in bold format. The default engine is automatically chosen depending on the file extension.

A small note: For compatibility with CSV writers, ExcelWriter serializes lists and dicts to strings before writing.

Last, if you use the with statement to create an ExcelWriter, you do not need to worry about saving the change in the end! An example code to create an ExcelWriter in the with statement is:

import pandas as pd df = pd.DataFrame()
excel_wb56_filepath = 'learn_excel_56.xlsx' # Syntax for create an ExcelWriter object in the with statement
# To create a new empty Excel file, learn_excel_56.xlsx with two new sheets!
with pd.ExcelWriter(excel_wb56_filepath) as writer: df.to_excel(writer, sheet_name='Class 5') df.to_excel(writer, sheet_name='Class 6')

After running the code, we create a new empty Excel file, learn_excel_56.xlsx with two new sheets, Class 5 and Class 6!

Meet pandas.DataFrame.to_excel

Hooray! Now, let’s look at the syntax and parameters of the pandas.DataFrame.to_excel method and get ourselves prepared for later examples!

Here is the syntax for pandas.DataFrame.to_excel:

# Syntax for pandas.DataFrame.to_excel
DataFrame.to_excel(excel_writer[, sheet_name='Sheet1'[, na_rep=''[, float_format=None[, columns=None[, header=True[, index=True[, index_label=None[, startrow=0[, startcol=0[, engine=None[, merge_cells=True[, encoding=None[, inf_rep='inf'[, verbose=True[, freeze_panes=None[, storage_options=None]]]]]]]]]]]]]]]])

To write a single object (pandas.DataFrame) to an Excel .xlsx file, it is only necessary to specify a target file name. To write to multiple sheets, it is necessary to create an ExcelWriter object with a target file name.

Parameter	Meta	Description
excel_writer	Required	Target file name or ExcelWriter object.
sheet_name	Optional	Name of sheet containing DataFrame. Default is "Sheet 1".
na_rep	Optional	Missing data representation. Default = ""
float_format	Optional	Format string for floating point numbers. For example float_format="%.2f" will format 0.1234 to 0.12.
column	Optional	Columns (in the DataFrame) to write.
header	Optional	Row to be considered as the header, excluding from the data part. Default = True, which means the first row. If None, no header.
index	Optional	Write row names (index). Default is True, which means show index. If set to be False, it means no index in the output Excel worksheet.
index_label	Optional	Column label for the index.
startrow	Optional	Upper left cell row to dump data frame. Default is 0.
startcol	Optional	Upper left cell column to dump data frame. Default is 0.
engine	Optional	String specifying the engine to use for writing. Default is xlwt for xls. xlsxwriter for xlsx if xlsxwriter is installed; otherwise openpyxl.
merge_cells	Optional	Write MultiIndex and Hierarchical Rows as merged cells.
encoding	Optional	Encoding of the resulting excel file. Only necessary for xlwt, other writers support Unicode natively.
inf_rep	Optional	Representation for infinity (there is no native representation for infinity in Excel).
verbose	Optional	Display more information in the error logs.
freeze_panes	Optional	Specifies the one-based bottom-most row and right-most column that is to be frozen.
storage_options	Optional	Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc.

Tip: Generally, you only need to use the four parameters in bold format. 

Please try to have an intuition for these parameters right now and I will cover the details for sheet_name, index, and index_label parameters in our next exciting examples!

Getting started

To use the pandas.DataFrame.to_excel method, you need to first install the pandas package in your command line:

$ pip install pandas 

Tip: you might need to use pip3 instead of pip, depending on your environment.

Given the engine compatibility mentioned above, you also need to install respective engine libraries. For example, to use openpyxl, you need to install this package on your command line:

$ pip install openpyxl

Tip: you might need to use pip3 instead of pip, depending on your environment.

By the way, if you have already installed Anaconda, you can skip this step

Dataset

In our examples, we will create Excel workbooks like the two Excel workbooks (.xlsx), learn_excel_12 and learn_excel_34, in our first Excel in Python series.

These workbooks have the same data structures and column names in each worksheet. For example, the following is the data in the Class_1 worksheet in the learn_excel_12 workbook.

One row in each sheet represents a student in each class and the columns represent the student’s ID, Major, and Score respectively. 

Concretely, learn_excel_12 has two worksheets, Class 1 and Class 2. And learn_excel_34 has two worksheets, Class 3 and Class 4.

You can find all datasets and codes in this tutorial here. The data in our example datasets are not actual data and are only used for educational purposes.

Save Data to One Worksheet

So, how can we save data to a single excel sheet? We can pass a target file name or create an ExcelWriter object to do so!

For our example, we can create a new Excel file, learn_excel_56 and write some student data into the Class 5 sheet, specifying through the parameter sheet_name.

import pandas as pd # For our writing mode example,
# we can create a new Excel file, learn_excel_56
# and write some student data into it.
excel_fp = 'learn_excel_56.xlsx'
class5_df = pd.DataFrame( {'ID': [51, 52], 'Major': ['English', 'Math'], 'Score': [98, 89]}) with pd.ExcelWriter(excel_fp, mode='w') as writer: class5_df.to_excel(writer, sheet_name='Class 5', index=False)

After running the code,  we can get the output Excel file:

Save Data to Multiple Worksheets

Likewise, we can save data to multiple worksheets by calling pandas.DataFrame.to_excel() method multiple times.

For our example, we can create a new Excel file, learn_excel_56 and write some student data into the Class 5 sheet and Class 6 sheet, specifying through the parameter sheet_name.

import pandas as pd # For our writing mode example,
# we can create a new Excel file, learn_excel_56
# and write some student data into it.
excel_fp = 'learn_excel_56.xlsx'
class5_df = pd.DataFrame( {'ID': [51, 52], 'Major': ['English', 'Math'], 'Score': [98, 89]})
class6_df = pd.DataFrame( {'ID': [61, 62], 'Major': ['History', 'Math'], 'Score': [78, 96]}) with pd.ExcelWriter(excel_fp, mode='w') as writer: class5_df.to_excel(writer, sheet_name='Class 5', index=False) class6_df.to_excel(writer, sheet_name='Class 6', index=False)

After running the code,  we can get the output Excel file:

Save Data to Multiple Workbooks

To get multiple workbooks, we can just create multiple pandas.ExcelWriter objects.

To give a quick example, let’s create two workbooks, learn_excel_78 and learn_excel_910.

import pandas as pd # let’s create two workbooks, learn_excel_78 and learn_excel_910.
wb78_fp = 'learn_excel_78.xlsx'
wb910_fp = 'learn_excel_910.xlsx' df = pd.DataFrame() with pd.ExcelWriter(wb78_fp, mode='w') as writer_78, pd.ExcelWriter(wb910_fp, mode='w') as writer_910: df.to_excel(writer_78, sheet_name='Class 7', index=False) df.to_excel(writer_78, sheet_name='Class 8', index=False) df.to_excel(writer_910, sheet_name='Class 9', index=False) df.to_excel(writer_910, sheet_name='Class 10', index=False)

After running the code,  we can get the output workbooks:

In the learn_excel_78 file, we can see that we have created two empty worksheets:

So far, we have understood the basic writing operations. Let’s move forward and deal with the most common issues—index and date

Deal with Index

You can set up the index and index’ column label when calling the pandas.DataFrame.to_excel() method.

Previously, our example codes set the index to be False, which means no index column in the output Excel file. Let’s see what will happen if we set the index to be True in the multiple worksheets scenario.

import pandas as pd # For our writing mode example,
# we can create a new Excel file, learn_excel_56
# and write some student data into it.
excel_fp = 'learn_excel_56.xlsx'
class5_df = pd.DataFrame( {'ID': [51, 52], 'Major': ['English', 'Math'], 'Score': [98, 89]})
class6_df = pd.DataFrame( {'ID': [61, 62], 'Major': ['History', 'Math'], 'Score': [78, 96]}) with pd.ExcelWriter(excel_fp, mode='w') as writer: class5_df.to_excel(writer, sheet_name='Class 5', index=True) class6_df.to_excel(writer, sheet_name='Class 6', index=True)

After running the code, we can see that we now have an index column, counting from zero.

On top of that, we can give a column name to the index column by specifying the parameter, index_label.

import pandas as pd # For our writing mode example,
# we can create a new Excel file, learn_excel_56
# and write some student data into it.
excel_fp = 'learn_excel_56.xlsx'
class5_df = pd.DataFrame( {'ID': [51, 52], 'Major': ['English', 'Math'], 'Score': [98, 89]})
class6_df = pd.DataFrame( {'ID': [61, 62], 'Major': ['History', 'Math'], 'Score': [78, 96]}) with pd.ExcelWriter(excel_fp, mode='w') as writer: class5_df.to_excel(writer, sheet_name='Class 5', index=True, index_label='No.') class6_df.to_excel(writer, sheet_name='Class 6', index=True, index_label='No.')

After running the code, we can see that we now have an index column with a name, “No.”!

Conclusion

That’s how to save data to Excel. This is the second article in our Excel in Python series.

We learned about Excel’s data structure and commonly used lingos and four popular ways to save data to Excel in Python, including pandas and openpyxl.

Finally, we looked at four actual use cases in pandas.DataFrame.to_excel, ranging from one Excel worksheet, multiple Excel worksheets, multiple Excel workbooks, and dealing with index cases.

I hope you enjoy all this, and stay tuned for our following Excel in Python article on saving data to Excel! Happy coding!

---

Before we learn about the money, let’s get this question out of the way:

What Is Flex?

Let’s have a look at the definition from the official Flex website:

“Flex is a powerful, open source application framework that allows you to easily build mobile applications for iOS, Android, and BlackBerry® Tablet OS devices, as well as traditional applications for browser and desktop using the same programming model, tool, and codebase.

You can use the Flex SDK to create a wide range of highly interactive, expressive applications. For example, a data visualization application built in Flex can pull data from multiple back-end sources and display it visually. Business users can drill down into the data for deeper insight and even change the data and have it automatically updated on the back end. A product configuration application can help customers navigate the process of selecting or customizing products online. And a self-service application can guide customers through an address change or help employees complete an otherwise complicated multi-step benefits enrollment.”

Now that you know about what it is, let’s have a look at what it earns next!

Annual Income

How much does a Flex Developer make per year?

The average annual income of a Flex Developer in the United States is $99,000 per year according to PayScale. Top earners make $141,000 and more in the US!

Here’s a table of different job roles (source):

Job Title	Income (avg)
Software Engineer	$92,500
Application Developer	$103,000
Senior Java Developer	$82,000
Senior Product Manager	$84,000
Software Developer, Web Applications	$102,000
Software Engineering / Development Director	$141,000
Sr. Software Engineer / Developer / Programmer	$110,000

Let’s have a look at the hourly rate of Flex Developers next!

Hourly Rate

Flex Developers are well-paid on freelancing platforms such as Upwork or Fiverr.

• Related Article: What’s the best freelancing platform?

If you decide to go the route as a freelance Flex Developer, you can expect to make between $30 and $40 per hour on Upwork (source). Assuming an annual workload of 2000 hours, you can expect to make between $60,000 and $80,000 per year.

Note: Do you want to create your own thriving coding business online? Feel free to check out our freelance developer course — the world’s #1 best-selling freelance developer course that specifically shows you how to succeed on Upwork and Fiverr!

Industry Demand

But is there enough demand? Let’s have a look at Google trends to find out how interest evolves over time (source):

Work Description

So, you may wonder: Flex Developer – what’s the definition?

Flex Developer Definition: A Flex Developer creates, edits, analyzes, debugs, and supervises the development of software written in the Flex programming framework. (Source)

Learning Path, Skills, and Education Requirements

Do you want to become a Flex Developer? Here’s a step-by-step learning path I’d propose to get started with Flex:

• Step 1: Introduction to Computer Science (~40 hours)
• Step 3: Introduction to Flex (~20 hours)
• Step 4: Introduction to Flex (Free Video Courses) (~10 hours)

You can find many additional computer science courses on the Finxter Computer Science Academy (flatrate model).

But don’t wait too long to acquire practical experience!

Even if you have little skills, it’s best to get started as a freelance developer and learn as you work on real projects for clients — earning income as you learn and gaining motivation through real-world feedback.

Tip: An excellent start to turbo-charge your freelancing career (earning more in less time) is our Finxter Freelancer Course. The goal of the course is to pay for itself!

Related Video

You can find more job descriptions for coders, programmers, and computer scientists in our detailed overview guide:

• Get Your Job in IT [Overview]

Related Income of Professional Developers

The following statistic shows the self-reported income from 9,649 US-based professional developers (source).

The average annual income of professional developers in the US is between $70,000 and $177,500 for various programming languages.

Question: What is your current total compensation (salary, bonuses, and perks, before taxes and deductions)? Please enter a whole number in the box below, without any punctuation. If you are paid hourly, please estimate an equivalent weekly, monthly, or yearly salary. (source)

The following statistic compares the self-reported income from 46,693 professional programmers as conducted by StackOverflow.

The average annual income of professional developers worldwide (US and non-US) is between $33,000 and $95,000 for various programming languages.

Here’s a screenshot of a more detailed overview of each programming language considered in the report:

Here’s what different database professionals earn:

Here’s an overview of different cloud solutions experts:

Here’s what professionals in web frameworks earn:

There are many other interesting frameworks—that pay well!

Look at those tools:

Okay, but what do you need to do to get there? What are the skill requirements and qualifications to make you become a professional developer in the area you desire?

Let’s find out next!

General Qualifications of Professionals

StackOverflow performs an annual survey asking professionals, coders, developers, researchers, and engineers various questions about their background and job satisfaction on their website.

Interestingly, when aggregating the data of the developers’ educational background, a good three quarters have an academic background.

Here’s the question asked by StackOverflow (source):

Which of the following best describes the highest level of formal education that you’ve completed?

However, if you don’t have a formal degree, don’t fear! Many of the respondents with degrees don’t have a degree in their field—so it may not be of much value for their coding careers anyways.

Also, about one out of four don’t have a formal degree and still succeeds in their field! You certainly don’t need a degree if you’re committed to your own success!

Freelancing vs Employment Status

The percentage of freelance developers increases steadily. The fraction of freelance developers has already reached 11.21%!

This indicates that more and more work will be done in a more flexible work environment—and fewer and fewer companies and clients want to hire inflexible talent.

Here are the stats from the StackOverflow developer survey (source):

Do you want to become a professional freelance developer and earn some money on the side or as your primary source of income?

Resource: Check out our freelance developer course—it’s the best freelance developer course in the world with the highest student success rate in the industry!

Other Programming Languages Used by Professional Developers

The StackOverflow developer survey collected 58000 responses about the following question (source):

Which programming, scripting, and markup languages have you done extensive development work in over the past year, and which do you want to work in over the next year?

These are the languages you want to focus on when starting out as a coder:

And don’t worry—if you feel stuck or struggle with a nasty bug. We all go through it. Here’s what SO survey respondents and professional developers do when they’re stuck:

What do you do when you get stuck on a problem? Select all that apply. (source)

Related Tutorials

To get started with some of the fundamentals and industry concepts, feel free to check out these articles:

• Freelance Developer – How to Code From Home and Earn Six Figures [Industry Report]
• How to Become a Python Freelancer—and Earn $1,000 on the Side? [A Step-by-Step Tutorial]
• How Adam Earns $5000 per Month as a Python Freelancer on Upwork [Month 4]
• Flex Wikipedia
• Learn Flex Google

Where to Go From Here?

Enough theory. Let’s get some practice!

Coders get paid six figures and more because they can solve problems more effectively using machine intelligence and automation.

To become more successful in coding, solve more real problems for real people. That’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?

You build high-value coding skills by working on practical coding projects!

Do you want to stop learning with toy projects and focus on practical code projects that earn you money and solve real problems for people?

If your answer is YES!, consider becoming a Python freelance developer! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.

If you just want to learn about the freelancing opportunity, feel free to watch my free webinar “How to Build Your High-Income Skill Python” and learn how I grew my coding business online and how you can, too—from the comfort of your own home.

Join the free webinar now!

Overview

Problem: How to check if a given value is NaN?

Here’s a quick look at the solutions to follow:

import math
import numpy as np
import pandas as pd x = float('nan')
print(math.isnan(x))
print(x != x)
print(np.isnan(x))
print(pd.isna(x))
print(not(float('-inf') < x < float('inf')))

So, what is a NaN value?

NaN is a constant value that indicates that the given value is Not a Number. It’s a floating-point value, hence cannot be converted to any other type other than float. We should know that NaN and Null are two different things in Python. The Null values indicate something which does not exist, i.e. is empty. But that is not the case with NaN.

We have to deal with NaN values frequently in Python especially when we deal with array objects or DataFrames. So, without further delay, let us dive into our mission critical question and have a look at the different methods to solve our problem.

Method 1: Using math.isnan()

The simplest solution to check for NaN values in Python is to use the mathematical function math.isnan().

math.isnan() is a function of the math module in Python that checks for NaN constants in float objects and returns True for every NaN value encountered and returns False otherwise.

Example:

# Importing the math module
import math # Function to check for NaN values
def isNaN(a): # Using math.isnan() if math.isnan(a): print("NaN value encountered!") else: print("Type of Given Value: ", type(a)) # NaN value
x = float('NaN')
isNaN(x)
# Floating value
y = float("5.78")
isNaN(y)

Output:

NaN value encountered!
Type of Given Value: <class 'float'>

In the above example, since x represents a NaN value, hence, the isNaN method returns True but in case of y , isNan returns False and prints the type of the variable y as an output.

Method 2: Hack NaN Using != Operator

The most unique thing about NaN values is that they are constantly shapeshifting. This means we cannot compare the NaN value even against itself. Hence, we can use the != (not equal to) operator to check for the NaN values. Thus, the idea is to check if the given variable is equal to itself. If we consider any object other than NaN, the expression (x == x) will always return True. If it’s not equal, then it is a NaN value.

Example 1:

print(5 == 5)
# True
print(['a', 'b'] == ['a', 'b'])
# True
print([] == [])
# True
print(float("nan") == float("nan"))
# False
print(float("nan") != float("nan"))
# True

Example 2:

# Function to check for NaN values
def not_a_number(x): # Using != operator if x != x: print("Not a Number!") else: print(f'Type of {x} is {type(x)}') # Floating value
x = float("7.8")
not_a_number(x)
# NaN value
y = float("NaN")
not_a_number(y)

Output:

Type of 7.8 is <class 'float'>
Not a Number!

Method 3: Using numpy.isnan()

We can also use the NumPy library to check whether the given value is NaN or not. We just need to ensure that we import the library at the start of the program and then use its np.isnan(x) method.

The np.isnan(number) function checks whether the element in a Numpy array is NaN or not. It then returns the result as a boolean array.

Example: In the following example we have a Numpy Array and then we will check the type of each value. We will also check if it is a NaN value or not.

import numpy as np arr = np.array([10, 20, np.nan, 40, np.nan])
for x in arr: if np.isnan(x): print("Not a Number!") else: print(x, ":", type(x))

Output:

10.0 : <class 'numpy.float64'>
20.0 : <class 'numpy.float64'>
Not a Number!
40.0 : <class 'numpy.float64'>
Not a Number!

TRIVIA

Let us try to perform some basic functions on an numpy array that involves NaN values and find out what happens to it.

import numpy as np arr = np.array([10, 20, np.nan, 40, np.nan])
print(arr.sum())
print(arr.max())

Output:

nan
nan

Now this can be a problem in many cases. So, do we have a way to eliminate the NaN values from our array object and then perform the mathematical operations upon the array elements? Yes! Numpy facilitates us with methods like np.nansum() and np.nanmax() that help us to calculate the sum and maximum values in the array by ignoring the presence of NaN values in the array.

Example:

import numpy as np arr = np.array([10, 20, np.nan, 40, np.nan])
print(np.nansum(arr))
print(np.nanmax(arr))

Output:

70.0
40.0

Method 4: Using pandas.isna()

Another way to solve our problem is to use the isna() method of the Pandas module. pandas.isna() is a function that detects missing values in an array-like object. It returns True if any NaN value is encountered.

Example 1:

import pandas as pd x = float("nan")
y = 25.75
print(pd.isna(x))
print(pd.isna(y))

Output:

True
False

Example 2: In the following example we will have a look at a Pandas DataFrame and detect the presence of NaN values in the DataFrame.

import pandas as pd df = pd.DataFrame([['Mercury', 'Venus', 'Earth'], ['1', float('nan'), '2']])
print(pd.isna(df))

Output:

 0 1 2
0 False False False
1 False True False

Method 5: By Checking The Range

We can check for the NaN values by using another NaN special property: limited range. The range of all the floating-point values falls within negative infinity to infinity. However, NaN values do not fall within this range.

Hence, the idea is to check whether a given value lies within the range of -inf and inf. If yes , then it is not a NaN value else it is a NaN value.

Example:

li = [25.87, float('nan')]
for i in li: if float('-inf') < float(i) < float('inf'): print(i) else: print("Not a Number!")

Output:

25.87
Not a Number!

Recommended read: Python Infinity

Conclusion

In this article, we learned how we can use the various methods and modules (pandas, NumPy, and math) in Python to check for the NaN values. I hope this article was able to answer your queries. Please stay tuned and subscribe for more such articles. 

Authors: SHUBHAM SAYON and RASHI AGARWAL

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Do you want to become a NumPy master? Check out our interactive puzzle book Coffee Break NumPy and boost your data science skills! (Amazon link opens in new tab.)

Summary: You can use consecutive single-line comments (using # character) to create a block of comments (multiline comments) in Python. Another way is to use """ quotes to enclose the comment block.

---

Problem Statement: How to create multiline comments in Python?

Other programming languages like C++, Java, and JavaScript, have an inbuilt mechanism (block comment symbols) for multiline comments, but there is no built-in mechanism for multiline comments in Python. Hence, the following code won’t work:

/* This is a multiline Comment in Python */

The above code will throw an error in Python. However, there are still some workarounds to using the multiline comments in Python. Let’s look at the different methods to do this in this article.

• Quick Trivia on Comments:
  • Comments are a very important part of every programming language as it explains the logic used in the code. The developers provide these comments to make it more readable and for the users to get a better understanding of the code. The comments don’t run as they are ignored by the interpreters and compilers. Comments also help us while we are debugging, i.e., when there are two lines of code, we can comment one out to prevent it from running.
• What is a multiline comment in Python?
  • A multiline comment in Python is a comment that generally expands to multiple lines, i.e., multiline comments are the comments that expand to two or more lines in the source code.

Method 1: Using Multiple Single Line Comments

We can use the multiple single-line comments to create multiline comments in Python. But first, you must know how to make a single-line comment is in Python. The Hash character (#) is used to make single-line comments in Python. The commented line does not get printed in the output.

Example:

# print("Hello Finxters")
print("Learning to create multiline comments in Python")

Output:

Learning to create multiline comments in Python

Now let’s create multiline comments using consecutive single line comments:

Example:

print("Learning to create multiline comments in Python")
# print("Hello Finxters")
# print("This is a")
# print("Multiline comment")
# print("in Python")
print("End of program")

Output:

Learning to create multiline comments in Python End of program

As we can see that the commented lines are ignored by the parser in Python, thereby creating a block of comments.

Discussion: Using single-line comments to comment out every line of a multiline comment individually becomes a very tedious process. Hence this method is not recommended to be used when you are not using any modern editor. However, most of the new code editors have a shortcut for block commenting in Python. You can just select a few lines of code using shift and the cursor keys and then press cmd + / (This shortcut may differ depending upon the editor you are using) to comment them out all at once. You can even uncomment them easily by simply selecting the block of comments and pressing the cmd + / keyboard shortcut.

Method 2: Using Docstrings Or Multiline Strings

We can create the multiline comments using multiline strings or docstrings in Python. This method has the same effect but is generally used for documentation strings, not block comments. However, if you want to comment things out temporarily, you can use this method. Python has two types of docstrings-
1) One-Line docstrings
2) Multiline docstrings.

To create a block comment, we use the multiline docstrings. Let’s create multiline comments using docstrings in the following example:

Example 1:

print("Learning to create multiline comments in Python") '''
print("Hello Finxters")
print("This is a")
print("Multiline comment")
print("in Python") '''
print("End of program")

Output:

Learning to create multiline comments in Python End of program

Example 2: Suppose you want to define a block comment inside a function using docstrings you have to do it the following way:

def multiply(x, y): res = x * y """ This is a multiline comment indented properly This function returns the result after multiplying the two numbers """ return res
print("The multiplication of the two numbers is", multiply(10, 5))

Output:

The multiplication of the two numbers is 50

• Caution:
  • You must always ensure that you have used the indentation for the first """ correctly; otherwise, you may get a SyntaxError.
  • Also, if you are opening a multiline comment using three double quotes """ then you must ensure that you enclose the block with exactly three double quotes as well. If you do nt follow this convention, you will get an error again. For example – if you open a multiline comment with three double quotes and close it using three single quotes, then you will get an error.

Example I: If you don’t intend """ properly, you may get the following error:

def multiply(x, y): res = x * y """ This is a multiline comment indented properly This function returns the result after multiplying the two numbers """ return res
print("The multiplication of the two numbers is", multiply(10, 5))

Output:

File "main.py", line 10 return res ^ IndentationError: unexpected indent

Example II: Let’s visualize what happens when there is a mismatch between the type of triple quotes used.

def multiply(x, y): res = x * y """ This is a multiline comment indented properly This function returns the result after multiplying the two numbers ''' return res
print("The multiplication of the two numbers is", multiply(10, 5))

Output:

 File "C:\Users\SHUBHAM SAYON\PycharmProjects\Finxter\General\rough.py", line 10 print("The multiplication of the two numbers is", multiply(10, 5)) ^ SyntaxError: EOF while scanning triple-quoted string literal

Note: You must always be careful where you place these multiline comments in the code. If it is commented right after a class definition, function, or at the start of a module, it turns into a docstring that has a different meaning in Python.

Example:

def multiply(x, y): """ This is a multiline comment made right after the function definition It now becomes a function docstring associated with the function object that is also accessible as runtime metadata """ res = x * y return res
print("The multiplication of the two numbers is", multiply(10, 3))

Output:

The multiplication of the two numbers is 30

The difference between the comment and the parser is that the comment is removed by the parser, whereas a docstring can be accessed programmatically at runtime and ends up in the byte code. 

Conclusion

Therefore, in this tutorial, we learned two ways of creating multiline comments in Python –
➨Using consecutive single-line comments.
➨Multiline strings (docstrings).

That’s all for this article. I hope you found it helpful. Please stay tuned and subscribe for more interesting articles and tutorials in the future. Happy learning!

Authors: Rashi Agarwal and Shubham Sayon

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Recommended Read:

• Python Multi-Line Strings
• Python Comments — 2-Minute Guide with Exercise

Problem Statement: How to define a method outside of class definition in Python?

Example:

# Given Class
class Demo:
# Given Method def foo(self): x = 10 return x

Can we create foo() outside of the class definition or maybe even in another module?

Introduction

We all know Python is an object-oriented programming language, and in Python, everything is an object including properties and methods. In Python, the class is like an object’s constructor for creating the objects. Thus, the variables can be defined inside the class, outside the class, and inside the methods in Python. The variables defined outside the class can be accessed by any method or class by just writing the variable name. So, in this article, we are going to learn how to define a method outside of the class definition.

What are Methods in Python?

As Python is an object-oriented programming language, it has objects that consist of properties. The attributes define the properties of these objects, and the behavior is defined using methods. Methods are reusable pieces of code called at any point in the program and are defined inside the class. Every method is associated with the class and can be invoked on the instances of that class.

For example, we can consider a class named ‘Students’ that contains properties like name, id , and rank. The class also holds behaviors like run, jump , and swim. Suppose an object Stud1 has the following properties:

Name- Finxter
Id – 1020
Rank- 1

Here’s how you can assign the values:

class Student: def __init__(self, name, id, rank): self.name = name self.id = id self.rank = rank def run(self): print(f'{self.name} is a cross country champion!') def jump(self): print(f'{self.name} with the following ID: {self.id} is a high jumper!') def swim(self): print(f'{self.name} secured rank {self.rank} in swimming.') stud1 = Student('Finxter', 1020, 1)
stud1.run()
stud1.jump()
stud1.swim()

Output:

Finxter is a cross country champion!
Finxter with the following ID: 1020 is a high jumper!
Finxter secured rank 1 in swimming.

The above example demonstrated the traditional way of adding functionality (methods) to a Python class. Here, the methods were defined inside the class body. Now, let’s say that you want to define a method outside the class body. How will you do so? Let’s dive into the different approaches to unearth the answers to this question.

Define The Method Outside and Use Inside Class Body

The idea here is to define the method outside the class and then use it inside the class body, as shown below.

Example 1:

# Defining the method outside the class
def foo(self): print("Method foo executed") x = 10 print("Value of x = ", x)
# Using the method inside the class body
class Demo: my_method = foo

You can also define a class first and then add a method or function to it from outside its body, as shown below.

Example 2:

# Define the class
class Demo: pass # Define the method outside the class def foo(self): print("Method foo executed ") # Pass the method to the class
Demo.method1 = foo

Caution: We can even define the functions, methods, and classes in different modules if we want to. However, it is advisable to use example 1 rather than example 2 (defining the class in one module, then importing it into another module and further adding methods to it dynamically) because the class objects may behave differently depending on whether the module has been imported or not.

There’s another way to define the function outside of a class and then further add it. But there is a difference in assigning the function to the instance object or the class. Look at the following example to understand the subtle difference:

class Demo1(object): def __init__(self, bar): self.func = 'Finxter' Demo1.funcbar = bar class Demo2(object): def __init__(self, bar): self.func = 'Finxter' self.funcbar = bar def bar(self): return 'Welcome' + self.func

Explanation: Let’s understand what’s happening here.

• In case of class Demo1 , funcbar is just like any other normal method that is bound to the instance of the class. Let’s have a look at what this looks like –

• In case of class Demo 2, funcbar is simply a reference to the bar function, i.e., it is not a bound function. Thus, we must pass the instance for this function for it to work properly.

Using Inheritance

You can even use the methods of a class in another class. In the following example we have a class Helper with certain methods defined within it. All the methods of the class Helper can be inherited by the class Demo as shown below.

class Helper(object): # Subtraction function def subs(self, a, b): return a - b # Addition function def add(self, a, b): return a + b # Multiplication function def mul(self, a, b): return a * b # Division function def div(self, a, b): return a / b
# Given class
class Demo(Helper): def __init__(self): Helper.__init__(self) print("The addition of numbers is", self.add(10, 5)) print("Subtraction of numbers is", self.subs(60, 15)) print("Multiplication of numbers is", self.mul(5, 9)) print("The division of numbers is", self.div(100, 50))
# Main method
if __name__ == '__main__': obj = Demo()

Output:

The addition of numbers is 15
Subtraction of numbers is 45
Multiplication of numbers is 45
The division of numbers is 2.0

Related Read: Inheritance in Python

Conclusion

To sum things up, it is absolutely possible to have functions outside classes in Python. If you want to gather a group of functions in one box then you can simply put them together in the same module. Further, you can nest modules within packages. It is recommended that you should use classes when you have to create a new datatype and don’t just use it to group functions together.

That’s it for this discussion and I hope it helped you. Please stay tuned and subscribe for more interesting articles and discussions in the future. Happy learning!

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Article by Shubham Sayon and Rashi Agarwal

Before we learn about the money, let’s get this question out of the way:

What Is Puppet?

Let’s have a look at the definition from this Puppet website (highlights be me):

“Puppet is an open source software configuration management and deployment tool. It’s most commonly used on Linux and Windows to pull the strings on multiple application servers at once. But you can also use Puppet on several platforms, including IBM mainframes, Cisco switches, and Mac OS servers.”

Popular alternatives to Puppet are:

• Ansible Developer — Income and Opportunity
• Chef Developer — Income and Opportunity

Personally, I’d prefer RedHat’s Ansible framework.

Now that you know about what it is, let’s have a look at what it earns next!

Annual Income

How much does a Puppet Developer make per year?

The average annual income of a Puppet Developer in the United States is $92,000 per year according to PayScale (source). Top earners make $160,000 and more in the US!

Here are different job descriptions using the Puppet skillset:

Let’s have a look at the hourly rate of Puppet Developers next!

Hourly Rate

Puppet Developers are well-paid on freelancing platforms such as Upwork or Fiverr.

• Related Article: What’s the best freelancing platform?

If you decide to go the route as a freelance Puppet Developer, you can expect to make between $25 and $60 per hour on Upwork (source). Assuming an annual workload of 2000 hours, you can expect to make between $50,000 and $120,000 per year.

Note: Do you want to create your own thriving coding business online? Feel free to check out our freelance developer course — the world’s #1 best-selling freelance developer course that specifically shows you how to succeed on Upwork and Fiverr!

Industry Demand

But is there enough demand? Let’s have a look at Google trends to find out how interest evolves over time (source):

Work Description

So, you may wonder: Puppet Developer – what’s the definition?

Puppet Developer Definition: A Puppet Developer is a DevOps engineer who automates repetitive administrative tasks in cloud and distributed environments (servers).

Learning Path, Skills, and Education Requirements

Do you want to become a Puppet Developer? Here’s a step-by-step learning path I’d propose to get started with Puppet:

• Step 1: Introduction to Computer Science (~40 hours)
• Step 3: Introduction to Puppet (~20 hours)
• Step 4: Introduction to Puppet (Free Video Courses) (~10 hours)

You can find many additional computer science courses on the Finxter Computer Science Academy (flatrate model).

But don’t wait too long to acquire practical experience!

Even if you have little skills, it’s best to get started as a freelance developer and learn as you work on real projects for clients — earning income as you learn and gaining motivation through real-world feedback.

Tip: An excellent start to turbo-charge your freelancing career (earning more in less time) is our Finxter Freelancer Course. The goal of the course is to pay for itself!

Related Video

You can find more job descriptions for coders, programmers, and computer scientists in our detailed overview guide:

• Get Your Job in IT [Overview]

Related Income of Professional Developers

The following statistic shows the self-reported income from 9,649 US-based professional developers (source).

The average annual income of professional developers in the US is between $70,000 and $177,500 for various programming languages.

Question: What is your current total compensation (salary, bonuses, and perks, before taxes and deductions)? Please enter a whole number in the box below, without any punctuation. If you are paid hourly, please estimate an equivalent weekly, monthly, or yearly salary. (source)

The following statistic compares the self-reported income from 46,693 professional programmers as conducted by StackOverflow.

The average annual income of professional developers worldwide (US and non-US) is between $33,000 and $95,000 for various programming languages.

Here’s a screenshot of a more detailed overview of each programming language considered in the report:

Here’s what different database professionals earn:

Here’s an overview of different cloud solutions experts:

Here’s what professionals in web frameworks earn:

There are many other interesting frameworks—that pay well!

Look at those tools:

Okay, but what do you need to do to get there? What are the skill requirements and qualifications to make you become a professional developer in the area you desire?

Let’s find out next!

General Qualifications of Professionals

StackOverflow performs an annual survey asking professionals, coders, developers, researchers, and engineers various questions about their background and job satisfaction on their website.

Interestingly, when aggregating the data of the developers’ educational background, a good three quarters have an academic background.

Here’s the question asked by StackOverflow (source):

Which of the following best describes the highest level of formal education that you’ve completed?

However, if you don’t have a formal degree, don’t fear! Many of the respondents with degrees don’t have a degree in their field—so it may not be of much value for their coding careers anyways.

Also, about one out of four don’t have a formal degree and still succeeds in their field! You certainly don’t need a degree if you’re committed to your own success!

Freelancing vs Employment Status

The percentage of freelance developers increases steadily. The fraction of freelance developers has already reached 11.21%!

This indicates that more and more work will be done in a more flexible work environment—and fewer and fewer companies and clients want to hire inflexible talent.

Here are the stats from the StackOverflow developer survey (source):

Do you want to become a professional freelance developer and earn some money on the side or as your primary source of income?

Resource: Check out our freelance developer course—it’s the best freelance developer course in the world with the highest student success rate in the industry!

Other Programming Languages Used by Professional Developers

The StackOverflow developer survey collected 58000 responses about the following question (source):

Which programming, scripting, and markup languages have you done extensive development work in over the past year, and which do you want to work in over the next year?

These are the languages you want to focus on when starting out as a coder:

And don’t worry—if you feel stuck or struggle with a nasty bug. We all go through it. Here’s what SO survey respondents and professional developers do when they’re stuck:

What do you do when you get stuck on a problem? Select all that apply. (source)

Related Tutorials

To get started with some of the fundamentals and industry concepts, feel free to check out these articles:

• Freelance Developer – How to Code From Home and Earn Six Figures [Industry Report]
• How to Become a Python Freelancer—and Earn $1,000 on the Side? [A Step-by-Step Tutorial]
• How Adam Earns $5000 per Month as a Python Freelancer on Upwork [Month 4]
• Puppet Wikipedia
• Learn Puppet Google

Where to Go From Here?

Enough theory. Let’s get some practice!

Coders get paid six figures and more because they can solve problems more effectively using machine intelligence and automation.

To become more successful in coding, solve more real problems for real people. That’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?

You build high-value coding skills by working on practical coding projects!

Do you want to stop learning with toy projects and focus on practical code projects that earn you money and solve real problems for people?

If your answer is YES!, consider becoming a Python freelance developer! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.

If you just want to learn about the freelancing opportunity, feel free to watch my free webinar “How to Build Your High-Income Skill Python” and learn how I grew my coding business online and how you can, too—from the comfort of your own home.

Join the free webinar now!

Before we learn about the money, let’s get this question out of the way:

What Is Terraform?

Let’s have a look at the definition from the official Terraform website:

“Terraform is an open-source infrastructure as code software tool that provides a consistent CLI workflow to manage hundreds of cloud services. Terraform codifies cloud APIs into declarative configuration files.”

Now that you know about what it is, let’s have a look at what it earns next!

Annual Income

How much does a Terraform Developer make per year?

The average annual income of a Terraform Developer in the United States is $116,000 per year according to PayScale (source). Top earners make $185,000 and more in the US!

Here’s a table with salary levels of jobs using the Terraform skillset to create more value for their companies and organizations:

Let’s have a look at the hourly rate of Terraform Developers next!

Hourly Rate

Terraform Developers are well-paid on freelancing platforms such as Upwork or Fiverr.

• Related Article: What’s the best freelancing platform?

If you decide to go the route as a freelance Terraform Developer, you can expect to make between $40 and $200 per hour on Upwork (source). Assuming an annual workload of 2000 hours, you can expect to make between $80,000 and $400,000 per year.

Note: Do you want to create your own thriving coding business online? Feel free to check out our freelance developer course — the world’s #1 best-selling freelance developer course that specifically shows you how to succeed on Upwork and Fiverr!

Industry Demand

But is there enough demand? Let’s have a look at Google trends to find out how interest evolves over time (source):

Work Description

So, you may wonder: Terraform Developer – what’s the definition?

Terraform Developer Definition: A Terraform Developer is a DevOps engineer who manages a possibly large number of cloud platforms — such as AWS, Azure, or Google Cloud — via remote access and the Terraform utility toolsets.

Learning Path, Skills, and Education Requirements

Do you want to become a Terraform Developer? Here’s a step-by-step learning path I’d propose to get started with Terraform:

• Step 1: Introduction to Computer Science (~40 hours)
• Step 2: Introduction to AWS (~20 hours)
• Step 3: Introduction to Azure (~20 hours)
• Step 4: Introduction to Google Cloud (~20 hours)
• Step 5: Introduction to Kubernetes (~20 hours)
• Step 6: Introduction to Terraform (~20 hours)
• Step 7: Introduction to Terraform (Free Video Courses) (~10 hours)

You can find many additional computer science courses on the Finxter Computer Science Academy (flatrate model).

But don’t wait too long to acquire practical experience!

Even if you have little skills, it’s best to get started as a freelance developer and learn as you work on real projects for clients — earning income as you learn and gaining motivation through real-world feedback.

Tip: An excellent start to turbo-charge your freelancing career (earning more in less time) is our Finxter Freelancer Course. The goal of the course is to pay for itself!

Related Video

You can find more job descriptions for coders, programmers, and computer scientists in our detailed overview guide:

• Get Your Job in IT [Overview]

Related Income of Professional Developers

The following statistic shows the self-reported income from 9,649 US-based professional developers (source).

The average annual income of professional developers in the US is between $70,000 and $177,500 for various programming languages.

Question: What is your current total compensation (salary, bonuses, and perks, before taxes and deductions)? Please enter a whole number in the box below, without any punctuation. If you are paid hourly, please estimate an equivalent weekly, monthly, or yearly salary. (source)

The following statistic compares the self-reported income from 46,693 professional programmers as conducted by StackOverflow.

The average annual income of professional developers worldwide (US and non-US) is between $33,000 and $95,000 for various programming languages.

Here’s a screenshot of a more detailed overview of each programming language considered in the report:

Here’s what different database professionals earn:

Here’s an overview of different cloud solutions experts:

Here’s what professionals in web frameworks earn:

There are many other interesting frameworks—that pay well!

Look at those tools:

Okay, but what do you need to do to get there? What are the skill requirements and qualifications to make you become a professional developer in the area you desire?

Let’s find out next!

General Qualifications of Professionals

StackOverflow performs an annual survey asking professionals, coders, developers, researchers, and engineers various questions about their background and job satisfaction on their website.

Interestingly, when aggregating the data of the developers’ educational background, a good three quarters have an academic background.

Here’s the question asked by StackOverflow (source):

Which of the following best describes the highest level of formal education that you’ve completed?

However, if you don’t have a formal degree, don’t fear! Many of the respondents with degrees don’t have a degree in their field—so it may not be of much value for their coding careers anyways.

Also, about one out of four don’t have a formal degree and still succeeds in their field! You certainly don’t need a degree if you’re committed to your own success!

Freelancing vs Employment Status

The percentage of freelance developers increases steadily. The fraction of freelance developers has already reached 11.21%!

This indicates that more and more work will be done in a more flexible work environment—and fewer and fewer companies and clients want to hire inflexible talent.

Here are the stats from the StackOverflow developer survey (source):

Do you want to become a professional freelance developer and earn some money on the side or as your primary source of income?

Resource: Check out our freelance developer course—it’s the best freelance developer course in the world with the highest student success rate in the industry!

Other Programming Languages Used by Professional Developers

The StackOverflow developer survey collected 58000 responses about the following question (source):

Which programming, scripting, and markup languages have you done extensive development work in over the past year, and which do you want to work in over the next year?

These are the languages you want to focus on when starting out as a coder:

And don’t worry—if you feel stuck or struggle with a nasty bug. We all go through it. Here’s what SO survey respondents and professional developers do when they’re stuck:

What do you do when you get stuck on a problem? Select all that apply. (source)

Related Tutorials

To get started with some of the fundamentals and industry concepts, feel free to check out these articles:

• Freelance Developer – How to Code From Home and Earn Six Figures [Industry Report]
• How to Become a Python Freelancer—and Earn $1,000 on the Side? [A Step-by-Step Tutorial]
• How Adam Earns $5000 per Month as a Python Freelancer on Upwork [Month 4]
• Terraform Wikipedia
• Learn Terraform Google

Where to Go From Here?

Enough theory. Let’s get some practice!

Coders get paid six figures and more because they can solve problems more effectively using machine intelligence and automation.

To become more successful in coding, solve more real problems for real people. That’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?

You build high-value coding skills by working on practical coding projects!

Do you want to stop learning with toy projects and focus on practical code projects that earn you money and solve real problems for people?

If your answer is YES!, consider becoming a Python freelance developer! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.

If you just want to learn about the freelancing opportunity, feel free to watch my free webinar “How to Build Your High-Income Skill Python” and learn how I grew my coding business online and how you can, too—from the comfort of your own home.

Join the free webinar now!

If you are considering investing time in learning about blockchain, it can seem a weighty undertaking.  There are a multitude of blockchain variants such as Bitcoin, Ethereum, Litecoin, and Dogecoin, and the terminology can be just as confusing. 

Four general types of blockchain networks are in use today: public, private, consortium, and permissioned.  Each has its own rules as to who may administer the blockchains and who can participate as a node.

Of the blockchain variants, Ethereum is appealing due to its community-driven nature, and it allows for many use cases including DeFi (decentralized finance), DApps (decentralized applications), and NFTs (non-fungible tokens). 

To begin working with Ethereum, I started looking for tutorials, and there are certainly a lot of them.  I visited several sites that turned up in my searches.  I began at ethereum.org, where it bills itself as the programmable blockchain.

The learning section on the Ethereum website contains well over 150 tutorials, and Solidity has over 40 entries. 

Info: Solidity is a high-level, object-oriented language for creating smart contracts (smart contracts are essentially the code or programs stored in the blockchain when sent from a wallet, whereupon the code executes), and is similar in appearance to Python or JavaScript. 

In no particular order, the following tutorials are among the most notable I found during my search.

Solidity for Beginners: Smart Contract Development Crash Course

Gregory McCubbin’s tutorial at Dapp University is a short tutorial for beginners, covering the basics of creating smart contracts.  It doesn’t linger on terminology and history but starts Solidity coding examples right away in the Remix IDE. 

So if you are eager to see Solidity in use, this quick tutorial (about an hour and a half) gets straight to the point.

Topics covered are language-related, such as constructors, data types, internal functions, etc.  This is a good tutorial for following along in an editor. 

I tried taking notes on paper, but decided better of it and resorted to my laptop.  The examples are easy to understand if you are already familiar with programming concepts, and the videos are short, about 15 – 20 minutes each. 

If you just want to get exposure to Solidity, you may find it worth the time, just don’t expect to use the code immediately. 

The examples in this tutorial are meant for explaining a few of the intricacies of Solidity, and is a small investment of your time to see what Solidity looks like with a few use cases.

Solidity Tutorial: How to Create NFTs with Hardhat

Learn how to leverage Node.js to build Ethereum Dapps and smart contracts.  If you are a JavaScript programmer, this tutorial steps you through the process of installing and configuring a Hardhat development environment with npm, the Node Package Manager.

This is a straightforward, text-based tutorial. 

There is little Solidity code, but the point is to avail yourself of the tools needed to write Solidity in a local, console-based, JavaScript IDE. 

You step through installing the environment, setting up a project, compiling a Solidity contract, and deploying it on the local network.  While you will need more knowledge of the Solidity language to write contracts and Dapps, this tutorial will help you set up your framework.

Solidity Smart Contract Tutuorials

Another text-based tutorial is at w3schools.io. 

It is more akin to documentation and use cases.  It doesn’t appear to be up to date, and uses varying Solidity versions. 

While there is no actual contract or Dapp built, the tutorial covers almost all of the features and keywords of the Solidity language. 

For instance, source code comments can be inline or block format like C++, and currency units are Wei, Finney, szabo, and ether.  I only listed this one for its brevity and still relevant information.

Hello World Smart Contract

Most beginners would probably want to check out this tutorial hosted at Ethereum.org. 

It is a full-stack guide using JavaScript and Solidity to build a Dapp. 

The only criticism is the need to create multiple 3^rd party accounts, such as an Alchemy account for access to Web3 APIs and to generate an API key. 

Hardhat is used as a console-based IDE, and online services Ropsten Etherscan and Metamask are used for blockchain testing and access to a crypto wallet, respectively.  It is the most complete text-based tutorial I’ve read so far.

Complete Guide On Solidity Programming

Part of a larger Blockchain tutorial for beginners, this tutorial steps through Solidity programming from the basics of pragma directives and data types to Smart Contract design patterns. 

There are both textual and video components to this tutorial. 

I found the video tutorial more instructive, while the textual content was good as a guide or outline.

If you learn by jumping in immediately, I suggest focusing on the video portion of this tutorial. In fact, if the thoroughness of this tutorial is any indication, the whole Blockchain playlist of 26 tutorials would be a very good starting point. It covers Etherium, Bitcoin, Dogecoin, Dapps, Merkle Tree, NFT vs. Crypto, and a lot more.  It is current and last updated Feb 16, 2022.

Learn Solidity: Absolute Beginners Solidity Tutorial

From Tutorialspoint comes this tutorial for novices. 

The course assumes prior knowledge of programming in general and some basic familiarity with blockchain. 

It is a little dated, but it covers everything about Solidity. 

Navigation is as easy as clicking a heading on the sidebar.

I was impressed with the amount of content, and this would make an excellent reference page for Solidity. It was like reading the C/C++ reference documentation.  This is a text-only tutorial, and its primary mode of delivery is by example. 

Once you start writing smart contracts in Solidity, this tutorial could be handy to refer back to.

Solidity Tutorial: A Detailed Introduction

This is the tutorial I would suggest starting with. 

It is a year old, but there isn’t much code involved.  It is a good introduction to Solidity and Ethereum in general.  Compiler installation is covered, as well as Remix IDE and Docker images.  A quick introduction to syntax is followed by types, variables, and a few use cases.

Coverage of Ethereum, EVM, and smart contracts is seldom included in the tutorials I’ve reviewed.  The author delivers these details to help define what will be covered in this Solidity tutorial, a method called pre-learning in educational sciences.

Smart Contract Tutorial: Create a lottery smart contract in Solidity

Block Explorer put out a series of videos on smart contracts on YouTube.  The content is focused on smart contracts, but ventures into Web3.js, Chainlink, and Solidity among other topics. 

This particular video covers some peculiarities in Solidity as a lottery smart contract is built.  Remix IDE on the Ethereum site is used to sandbox the contract, so you can test functionality while refinements are made.  This video succeeds in giving a good overview of Solidity basics in about 45 minutes.

Web3 Tutorial: Solidity Smart Contracts

The penultimate tutorial I considered for this article is from Part Time Larry, and is the third of a series of ten recent tutorials on smart contracts.  The tutorial is organized as a series of posts at hackingthemarkets.com. 

The posts each relate to a topic in current Web3 practices regarding smart contracts.

This particular post has the viewer follow along, writing a smart contract within a full-stack environment that is already in place.  It’s a very thorough video with a textual component.  Seeing the tools used helps the viewer become familiar with them as they move back and forth between editor and console.

Solidity Blockchain Smart Contracts: Beginner to Expert

This final tutorial comes from one of my favorite tutorial websites, freecodecamp.org.  I think their work creating free tutorials to help people learn to code is exceptional. 

This tutorial is exceptional as well, it’s sixteen hours of coursework packed into one video.  I am still going through various parts, rewinding and reviewing areas I need to brush up on. 

I will be using this course for a few months I’m sure.

Almost everything in all the other videos is covered here with some additional content.  In addition to that, Python is used in this course, so it is relevant to my current endeavors as a Python developer. 

This tutorial/course should be considered if the previous tutorials were interesting and you are thinking about working in this niche as a developer.

Conclusion

I hope these tutorials are useful, and shed some light on what Solidity is used for. 

Most simply touch on what Solidity and smart contracts are, but a few of them are full courses. 

Whatever the level of interest you have in blockchain and Ethereum, with the investment of a little time, you are sure to find something of note in them.

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Learn Solidity Course

Solidity is the programming language of the future.

It gives you the rare and sought-after superpower to program against the “Internet Computer”, i.e., against decentralized Blockchains such as Ethereum, Binance Smart Chain, Ethereum Classic, Tron, and Avalanche – to mention just a few Blockchain infrastructures that support Solidity.

In particular, Solidity allows you to create smart contracts, i.e., pieces of code that automatically execute on specific conditions in a completely decentralized environment. For example, smart contracts empower you to create your own decentralized autonomous organizations (DAOs) that run on Blockchains without being subject to centralized control.

NFTs, DeFi, DAOs, and Blockchain-based games are all based on smart contracts.

This course is a simple, low-friction introduction to creating your first smart contract using the Remix IDE on the Ethereum testnet – without fluff, significant upfront costs to purchase ETH, or unnecessary complexity.