All examples for Data Type Validator & Fixer

European Number Format Conversion

Demonstrates handling of European number formats where periods are thousands separators and commas are decimal separators (1.234,56 = 1234.56). Also shows ID column detection and preservation logic for columns containing 'ID' in the name.

Input

What you provide to the skill

fix types - data has european numbers: Customer_ID,Order_ID,Revenue,Quantity
00123,A-001,“1.234,56”,10
00456,A-002,“2.567,89”,5

Output

What the skill generates for you

============================================================
TYPE VALIDATION REPORT
============================================================

CURRENT DATA TYPES:
Customer_ID     int64
Order_ID       object
Revenue        object
Quantity        int64
dtype: object

CURRENT DATA:
   Customer_ID Order_ID   Revenue  Quantity
0          123    A-001  1.234,56        10
1          456    A-002  2.567,89         5

============================================================
COLUMNS TO PRESERVE AS STRINGS
============================================================

Detected ID columns (preserving leading zeros):
  - Customer_ID: contains 'ID', has leading zeros (00123, 00456)
  - Order_ID: contains 'ID'

============================================================
CONVERTING EUROPEAN NUMBER FORMAT
============================================================

1. Converting 'Revenue' column:
   Original type: object (European format with quotes)
   Detected format: '1.234,56' (period=thousands, comma=decimal)
   Transformations:
     - Remove quotes
     - Remove period (thousands separator)
     - Replace comma with period (decimal separator)
   New type: float64
   Successful: 2  |  Failed: 0

2. Converting 'Quantity' column:
   Original type: int64
   Already correct numeric type
   No conversion needed

============================================================
FINAL DATA TYPES
============================================================
Customer_ID      int64
Order_ID        object
Revenue        float64
Quantity         int64
dtype: object

============================================================
CLEANED DATA PREVIEW
============================================================
   Customer_ID Order_ID  Revenue  Quantity
0          123    A-001  1234.56        10
1          456    A-002  2567.89         5

============================================================
DATA SUMMARY
============================================================
Total rows: 2
Revenue range: 1234.56 to 2567.89
Revenue total: 3802.45
Total quantity: 15

Exported: /workspace/data_cleaned.csv

============================================================
CLEANED CSV CONTENT
============================================================
Customer_ID,Order_ID,Revenue,Quantity
123,A-001,1234.56,10
456,A-002,2567.89,5
View all examples for Data Type Validator & Fixer

About This Skill

Validate and fix data types in datasets - convert dates stored as strings, numbers with currency symbols, percentages, and booleans to proper types with error handling.

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