Apache Spark is ideal when working with large datasets that exceed the memory limits of a single machine, or when you need to process data in real-time. For example, if you're analyzing logs from a large web application with terabytes of data that require complex transformations and aggregations, Spark's distributed processing capabilities will enable efficient computation, whereas Pandas would struggle with such a volume.
Pandas is a powerful library for data manipulation and analysis in Python, ideal for tasks that can fit in memory. Use Pandas when you have a small to medium-sized dataset that you can easily load into memory and need fast, complex data analysis and manipulation. For example, if you have a CSV file with user data of a few thousand rows, Pandas would be efficient and straightforward for cleaning, transforming, and visualizing this data.