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Efficient “For Loop” in Python, every programmer should know
Introduction
Looping is an inherent skill in our programming repertoire. When we familiarize ourselves with any programming language, loops become a fundamental and easily interpretable concept. Similarly, when working with Python, especially when iterating through dataset rows, our instinct is to consider implementing loops.
However, loops can become inefficient when dealing with sizable datasets, significantly slowing down DataFrame iteration. Should we entirely avoid using loops, or are there strategies to tackle this challenge?
The good news is that there are indeed solutions!
In this blog post, we will explore various approaches to iterate through large pandas DataFrames, examining associated runtimes for each looping method.
By the end of this blog, you will be well-informed about the most effective looping techniques for handling larger datasets.
The Dataset for Experimentation
We will be using a DataFrame with 6 Million rows and 4 columns. Each column will be assigned a random integer between 0 and 50.
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randint(0, 50, size=(6000000, 4)), columns=('a','b','c','d'))…