Data Profiling Results
Columns and Rules
Dataset: manual_demo · Rows: 4 · Columns: 6 · Overall Missing: 12.50%
Data Preview (Top 4 rows)
idagescoredatecityflag
AB12345629.088.52025-12-01HalifaxTrue
CD00000135.091.22025-12-02DartmouthFalse
EF999999nan76.02025-12-02HalifaxTrue
42.091.2BedfordTrue
Columns Value Distribution % Null # Null % Distinct # Distinct % Non-distinct # Non-distinct # Patterns % of Top Pattern Minimum Len Maximum Len profile_type
id
25.00% 1 100.00% 3 0.00% 0 1 100.00% 0 8 string
age
25.00% 1 100.00% 3 0.00% 0 1 100.00% 0 4 numeric
score
0.00% 0 75.00% 3 25.00% 1 1 100.00% 4 4 numeric
date
25.00% 1 66.67% 2 33.33% 1 1 100.00% 0 10 datetime
city
0.00% 0 75.00% 3 25.00% 1 2 75.00% 7 9 string
flag
0.00% 0 50.00% 2 50.00% 2 2 75.00% 4 5 boolean
Column JSON: id
{
  "name": "id",
  "profile_type": "string",
  "pandas_dtype": "object",
  "row_count": 4,
  "missing_count": 1,
  "missing_rate": 0.25,
  "non_null_count": 3,
  "non_null_rate": 0.75,
  "distinct_count": 3,
  "duplicate_count": 0,
  "min": null,
  "max": null,
  "mean": null,
  "median": null,
  "std": null,
  "min_len": 0,
  "max_len": 8,
  "avg_len": 8.0,
  "pattern_summary": [
    {
      "pattern": "L2D6",
      "count": 3,
      "pct": 1.0
    }
  ],
  "top_values": [
    {
      "value": "AB123456",
      "count": 1,
      "pct": 0.25
    },
    {
      "value": "CD000001",
      "count": 1,
      "pct": 0.25
    },
    {
      "value": "EF999999",
      "count": 1,
      "pct": 0.25
    },
    {
      "value": "<NA>",
      "count": 1,
      "pct": 0.25
    }
  ],
  "type_suggestion": {
    "suggested_type": "STRING",
    "max_len": 8,
    "confidence": 1.0
  }
}