Timestamp Parsing for time-series Data Analysis with Pandas and Python

In this article, we’ll examine the performance and applicability of different timestamp parsing methods on different types of datasets. We’ll see when to blindly use Pandas and when to use something else.

Much of the data that we generate today is in the form of time-series data. And analysis of this data often relies on representing the timestamps of the data in a structure that is amenable to time-based slicing and dicing. In standard Python and popular data analysis libraries such as Numpy and Pandas, there are dedicated data types to store time-based information. However, incoming timestamps are often strings with different formats. And parsing these strings into time-based data types is a time-consuming and sometimes tedious process.

Data types for time-related data in Pandas. Image from pandas.pydata.org.

In standard Python, a common way of parsing timestamp strings that have a known format is the time module’s strptime method (similar interface to C’s strptime).

However, since most data scientists have to do much more with a dataset than parse timestamp strings, powerful libraries like Pandas have become very popular. And in Pandas, the most common way of parsing timestamp strings is the to_datetime method. This method provides a lot of flexibility and it can even infer formats automatically. Therefore, many people use it almost blindly.

In this article, we’ll examine the performance and applicability of different timestamp parsing methods on different types of datasets. We’ll see when to blindly use Pandas and when to use something else.

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