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Does pandas iterrows have performance issues?

The solution for "does pandas iterrows have performance issues". By Pranit Sharma Last updated : September 23, 2023

While working on pandas DataFrame or CSV files especially when the data set is huge (2-3 million rows), the experience of using iterrows is not good. One of the reasons for this bad experience is because when we deal with a huge dataset, there is a lot of data type mixing which iterrows find difficult to tackle.

One can also observe that operations like apply, vectorization, etc are much quicker even after the fact that there is some row-by-row iteration. So, the question is when to use iterrows and when not, and in this article, we will discuss different operations and their significance.

Experts say that there is an order of precedence to perform certain operations in data analysis, the order is as follows:

• vectorization
• apply
• itertuples
• iterrows
• loc, iloc, etc

vectorization is the best fit operation in the case of DataFrames and it is faster to use other methods.

On the other hand, we use apply when we want to pass a special function to return a value to modify the dataframe. It is a quick operation to modify the values of DataFrame by applying a function to it.

Iterrows on the other hand encapsulate the data into series, which may not be suitable for the programmers and analysts and hence you should use another method unless you have no other option than iterrows.

Conclusion: Use vector operations when you work in NumPy and Pandas rather than using scalar operations and the only reason behind this is scalar operations are much slower which consumes a huge amount of time.

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