3 Practical Differences You Need To Know In Pandas


Data Science with Python

The differences you need to know for effective data analytics

Photo by Alessandro D’Antonio on Unsplash

Choose the correct data type conversion method for time-efficient data analysis!

In my last two articles, you can explore the tricks and tips for working with date-time or time-series data in Python and Pandas.

When working with time-series data in Pandas, you can use either pandas.Series.astype() or pandas.to_datetime() to convert date-time strings to datetime64[ns] data type. Both these methods return exactly the same output.

However, there is a significant difference in their performance, flexibility, and the way they handle errors. And choosing the correct method for data type conversion will be easier when you understand these differences.

In this article, you’ll learn about these 3 practical differences between pandas.Series.astype() and to_datetime() methods. Here is a quick overview of the topics you can explore in this article —

· Performance Differences Between astype() and to_datetime()
·
Handling of Dates and Time
·
Error Handling

Let’s get started!

The comparison of two methods or functions in the programming is incomplete without comparing their efficiency. And one of the best methods to compare efficiency is in terms of time.

The performance of the method helps you understand how efficiently and quickly that method works, i.e. in this case converts the data type to datetime64[ns].

It can be a critical aspect when you are working on an analytics project and processing a really huge amount of data.

One of the simplest ways to measure performance is execution time. The method that takes the lowest time to execute will be certainly time-efficient, and you can say it performs better than others.

Let’s use the same example as my previous article — Read the dummy dates sales data in a…



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