All examples for Data Type Validator & Fixer

Survey Data with Mixed Missing Values

Handles survey response data with multiple missing value representations (N/A, n/a), percentage strings, and mixed date formats (YYYY-MM-DD and MM/DD/YYYY). Shows how the skill standardizes missing values to NaN and converts percentages to decimals.

Input

What you provide to the skill

check data types in this survey data: ID,Score,Completion,Response_Rate
R001,4,2024-01-10,85%
R002,N/A,2024-01-11,n/a
R003,5,01/12/2024,90%

Output

What the skill generates for you

============================================================
INITIAL INSPECTION
============================================================

Current data types:
ID                object
Score            float64
Completion        object
Response_Rate     object
dtype: object

Sample values:
     ID  Score  Completion Response_Rate
0  R001    4.0  2024-01-10           85%
1  R002    NaN  2024-01-11           NaN
2  R003    5.0  01/12/2024           90%

============================================================
STANDARDIZING MISSING VALUES
============================================================


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

1. Converting 'Score' column:
   Original type: object
   New type: float64
   Successful: 2  |  Failed: 1
   Note: 1 values could not be converted (now NaN)

2. Converting 'Completion' column:
   Original type: object
   Detected formats: YYYY-MM-DD, MM/DD/YYYY
   Using: format='mixed' for auto-detection
   New type: datetime64[ns]
   Successful: 3  |  Failed: 0

3. Converting 'Response_Rate' column:
   Original type: object (percentage strings)
   New type: float64
   Transformations: removed %, divided by 100
   Successful: 2  |  Failed: 1

============================================================
FINAL DATA TYPES
============================================================
ID                       object
Score                   float64
Completion       datetime64[ns]
Response_Rate           float64
dtype: object

Cleaned data preview:
     ID  Score Completion  Response_Rate
0  R001    4.0 2024-01-10           0.85
1  R002    NaN 2024-01-11            NaN
2  R003    5.0 2024-01-12           0.90

============================================================
DATA SUMMARY
============================================================
Total records: 3
Date range: 2024-01-10 00:00:00 to 2024-01-12 00:00:00
Score: Mean=4.50, Range=[4-5]
Response Rate: Mean=87.5%

Exported: /tmp/survey_data_cleaned.csv

Cleaned CSV:

ID,Score,Completion,Response_Rate
R001,4.0,2024-01-10,0.85
R002,,2024-01-11,
R003,5.0,2024-01-12,0.9
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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