Python SDK

fs.profile()

SDK Reference: statistical profiling

python

1def profile(
2    source: object, 
3    *, 
4    max_correlation_columns: int = 100,
5    max_frequency_table_size: int = 1000
6) -> ProfileResult:

Profile a Dataset or tabular source directly, executing vectorized summaries and returning a strongly-typed, serializable ProfileResult.

Arguments

source: Dataset | str | DataFrame. Pre-loaded Dataset or file/data source.
max_correlation_columns: int (default 100). Column cap for Pearson correlation computations.
max_frequency_table_size: int (default 1000). Maximum unique categories to track in frequency table summaries.

Example

python

1import featuresmith as fs
2
3profile = fs.profile("customers.csv", max_correlation_columns=50)
4
5# Inspect column summaries
6print(profile.column_profiles["age"].missing_count)
7print(profile.dataset_summary.row_count)

Python SDK

fs.profile()

SDK Reference: statistical profiling

python

1def profile(
2    source: object, 
3    *, 
4    max_correlation_columns: int = 100,
5    max_frequency_table_size: int = 1000
6) -> ProfileResult:

Profile a Dataset or tabular source directly, executing vectorized summaries and returning a strongly-typed, serializable ProfileResult.

Arguments

source: Dataset | str | DataFrame. Pre-loaded Dataset or file/data source.
max_correlation_columns: int (default 100). Column cap for Pearson correlation computations.
max_frequency_table_size: int (default 1000). Maximum unique categories to track in frequency table summaries.

Example

python

1import featuresmith as fs
2
3profile = fs.profile("customers.csv", max_correlation_columns=50)
4
5# Inspect column summaries
6print(profile.column_profiles["age"].missing_count)
7print(profile.dataset_summary.row_count)