Dataset Comparison

pandas-profiling can be used to compare multiple version of the same dataset. This is useful when comparing data from multiple time periods, such as two years. Another common scenario is to view the dataset profile for training, validation and test sets in machine learning.

The following syntax can be used to compare two datasets:

from pandas_profiling import ProfileReport

train_df = pd.read_csv("train.csv")
train_report = ProfileReport(train_df, title="Train")

test_df = pd.read_csv("test.csv")
test_report = ProfileReport(test_df, title="Test")

comparison_report = train_report.compare(test_report)
comparison_report.to_file("comparison.html")

The comparison report uses the title attribute out of Settings as a label throughout. The colors are configured in settings.html.style.primary_colors. The numeric precision parameter settings.report.precision can be played with to obtain some additional space in reports.

In order to compare more than two reports, the following syntax can be used:

from pandas_profiling import ProfileReport, compare

comparison_report = compare([train_report, validation_report, test_report])

# Obtain merged statistics
statistics = comparison_report.get_description()

# Save report to file
comparison_report.to_file("comparison.html")

Note that this functionality only ensures the support report comparison of two datasets. It is possible to obtain the statistics - the report may have formatting issues. One of the settings that can be changed is settings.report.precision. As a rule of thumb, the value 10 can be used for a single report and 8 for comparing two reports.