Profiling high volumes of data with the pandas data profiler
Pandas profiling is a powerful library for generating detailed reports on datasets. However, for large datasets, the profiling process can become time-consuming and memory-intensive. When dealing with large datasets, you may need to consider a few strategies to optimize the profiling process:
- Sampling: Instead of profiling the entire dataset, you can take a random sample of the data to generate the report. This can significantly reduce the computation time and memory requirements while still providing a representative overview of the dataset:
from ydata_profiling import ProfileReport sample_df = iris_data.sample(n=1000) # Adjust the sample size as per your needs report = ProfileReport(sample_df)
- Subset selection: If you’re interested in specific columns or subsets of the dataset, you can select only those columns for profiling. This reduces the computational load and narrows down the focus to the...
