Working with large data files
One of the advantages of using pandas is that it provides data structures for in-memory analysis, which results in a performance advantage when working with data. However, this advantage can also become a constraint when working with large datasets, as the amount of data you can load is limited by the available memory. When datasets exceed the available memory, it can lead to performance degradation, especially when pandas creates intermediate copies of the data for certain operations.
In real-world scenarios, there are general best practices to mitigate these limitations, including:
- Sampling or loading a small number of rows for your Exploratory Data Analysis (EDA): Before applying your data analysis strategy to the entire dataset, it is a good practice to sample or load a small number of rows. This allows you to get a better understanding of your data, gain some intuition, and identify unnecessary columns that can be eliminated, thus reducing the overall...
