Iteration in Python | enumerate (), element (), e.g. enditer (), iterrows ()

Contents

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Introduction

An iteration is an object that repeats identical or similar tasks without making mistakes. In a way, we can say that repeated execution of a set of statements is what iteration is all about. Piton has several language features to help perform the iteration task.

As object, the iterator counts a series of values ​​that can be iterated over. The lists, tuples, dictionaries, strings and sets are all iterable objects. Son iterables containers from which you can get an iterator.

In the next topic, we will see a summary of the different iteration processes.

Loop using enumerate ()

Using a for loop to iterate over a list only gives us access to all the items in the list in each execution, one after another. If one also wants to enter the index data, then, where is the element of the list that we are iterating over, we can use enumerate().

As an example, watch how by The loop was converted by creating a list of areas:

# areas list
areas = [11.25, 18.0, 20.0, 10.75, 9.50]

# Change for loop to use enumerate() and update print()
for x, y in enumerate(areas) :
    print("room ", str(x), ": ", str(Y))

Production:

room 0: 11.20
room 1: 18.0
room 2: 20.0
room 3: 10.75
room 4: 9.5

Loop over dictionary – items ()

In Python 3, we need the items() method to loop over a dictionary. On each iteration, "the capital of x is y" will be printed out, where x is the key and y is the value of the pair.
# Definition of dictionary
europe = {'spain':'madrid', 'france':'paris', 'germany':'berlin',
 'norway':'oslo', 'italy':'rome', 'poland':'warsaw', 'austria':'vienna' }

# Iterate over europe
for x, y in europe.items():
    print("the capital of ", str(x), " is ", str(Y))

Production:

the capital of norway is oslo
the capital of poland is warsaw
the capital of italy is rome
the capital of spain is madrid
the capital of austria is vienna
the capital of germany is berlin

Bucle sobre matriz Numpy – e.g. enditer ()

Si estamos tratando con una matriz 1D Numpy, recorrer todos los ítems puede ser tan simple como:

for x in my_array :

Si estamos tratando con una matriz 2D Numpy, is more complex. Una matriz 2D está formada por varias matrices 1D. Para iterar explícitamente sobre todos los ítems separados de una matriz multidimensional, necesitaremos esta sintaxis:

for x in np.nditer(my_array) :

Then, escribimos un bucle for que itera sobre todos los ítems de np_height e imprime “x pulgadas” para cada elemento, donde x es el valor de la matriz.

# Import numpy as np
import numpy as np

# For loop over np_height
for x in np_height:
    print(x, "inches")

# For loop over np_baseball
for n in np.nditer(np_baseball):
    print(n)

Production:

74 inches
74 inches
72 inches
72 inches
73 inches
69 inches
69 inches
71 inches
76 inches
71 inches
73 inches…..

Recorriendo iterrows ():

Usando iterrows () para iterar sobre cada observación de un Pandas DataFrame. Here, estamos usando un bucle for para agregar una nueva columna, llamada PAÍS, que contiene una versión en mayúsculas de los nombres de los países en la columna “country”. Estamos usando el método de cadena superior() for this.

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Code for loop that adds COUNTRY column
for lab, row in cars.iterrows():
    cars.loc[lab, "COUNTRY"] = row['country'].upper()

# Print cars
print(cars)

Utilizar iterrows () para iterar sobre cada observación de un Pandas DataFrame es fácil de comprender, pero no muy eficiente. In each iteration, estamos creando una nueva serie Pandas en Python. Si queremos agregar una columna a un DataFrame llamando a una función en otra columna, el método iterrows () en combinación con un bucle for no es la forma preferida de hacerlo. Instead, queremos utilizar request()

A continuación usaremos el request() versión para obtener el mismo resultado en el DataFrame:

# Use .apply(str.upper)
cars["COUNTRY"] = cars["country"].apply(str.upper)
print(cars)

Production:

cars_per_cap country drives_right (US, COUNTRY)
US 809 United States True UNITED STATES
AUS 731 Australia False AUSTRALIA
JPN 588 Japan False JAPAN
IN 18 India False INDIA
RU 200 Russia True RUSSIA
MOR 70 Morocco True MOR

Podemos usar las herramientas de iteración anteriores para trabajar con la iteración en Python de una manera más efectiva. Este fue solo el resumen de la iteración. Se puede trabajar con diferentes ejemplos.

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