Export the profile as a structured dictionary.
Usage
A profile is worth keeping when it serves as a baseline: a record of what a table looked like at one point, to compare later scans against, or to store alongside the table in a catalog. to_dict() returns the whole profile as plain Python data, which from_dict() turns back into a DataScan. For a JSON string or file, use to_json() or save_to_json().
The profile includes the first five non-missing values of each column (sample_data). If the table holds sensitive values, keep that in mind before storing or sharing the result.
dict[str, Any]"metadata" holds the table name, the row count, the list of column names, and "skipped_columns" (a dictionary mapping each column of an unsupported type to its type). "columns" holds a list with one entry per summarized column, each a dictionary with "colname", "coltype", "sample_data", and "statistics" (a dictionary mapping each statistic’s name to its value).
Let’s scan a small table of sensor readings, where one reading is missing.
import pointblank as pb
import polars as pl
readings = pl.DataFrame(
{"sensor": ["T1", "T2", "T3", "T4"], "temp_c": [21.4, 22.1, None, 20.8]}
)
profile = pb.DataScan(readings, tbl_name="readings").to_dict()
profile["metadata"]{'table_name': 'readings',
'row_count': 4,
'columns': ['sensor', 'temp_c'],
'skipped_columns': {}}Each column has its own entry, with the statistics in a nested dictionary:
{'n_missing': 1,
'n_unique': 4,
'mean': 21.433333333333334,
'median': 21.4,
'std': 0.6506407098647716,
'min': 20.8,
'max': 22.1,
'p05': 20.806,
'q_1': 21.1,
'q_3': 21.75,
'p95': 22.03,
'iqr': 0.6499999999999986}The missing reading is counted in n_missing and left out of the other statistics. The statistics keep their Python types (the minimum and maximum of a date column, for example, are datetime.date values), and passing the dictionary to from_dict() gives back the same profile.