Python pandas quickly solve the problem of empty lists

In data processing, null or missing values are very common, especially when dealing with large-scale data from multiple sources. The pandas library in Python provides us with rich functionality to handle missing values or empty data, but when dealing with list data, the issue of empty lists can become complex. Empty lists not only affect the accuracy of data analysis, but also lead to code errors or logical failures. Therefore, how to efficiently and accurately handle empty lists is one of the skills that pandas users must master.

Definition of Empty List

An empty list is represented in Python as[]It is a list without any elements. In pandas' DataFrame or Series, an empty list may appear as a value for a column or row. Compared to NumpyNaNDifferent, an empty list is a valid Python object, so different methods need to be used to identify and handle it when processing it.

The problem of empty lists typically occurs in complex datasets containing nested lists or in scenarios from irregular data sources. They not only occupy space, but also affect subsequent calculations and analysis, so they require special handling.

Create a pandas DataFrame containing an empty list

To better understand how to handle empty lists, first create a pandas DataFrame that contains the empty list.

The following code demonstrates how to build an example data box containing an empty…