Updated Prompt

app.py

@@ -92,20 +92,16 @@ def describe(df):
 def validate_pandera(tests, df):
     validation_results = []
 
-    # Loop through each test rule and validate each column separately
     for test in tests:
         column_name = test['column_name']
-        rule = eval(test['pandera_rule'])  # Evaluate the Pandera column rule
-
         try:
-            # Apply the rule to the column and validate
-            validated_column = rule(df[[column_name]])  # Validate the specific column
+            rule = eval(test['pandera_rule'])  
+            validated_column = rule(df[[column_name]])  
             validation_results.append({
             "Columns": column_name,
             "Result": "✅ Pass"
             })
         except Exception as e:
-            # If validation fails, catch the exception and mark the column as 'Fail'
             validation_results.append({
             "Columns": column_name,
             "Result": f"❌ Fail - {str(e)}"

prompt.py

@@ -8,6 +8,7 @@ Follow this process:
 2. **For each column**, create a validation rule using Pandera syntax.
     Here are the valid pandera check class methods DO NOT USE ANYOTHER METHODS OTHER THAN THE BELOW GIVEN METHODS:
     DO NOT USE SINGLE backslashes \  BUT USE DOUBLE backslashes  \\ IN PATTERN
+    USE CORRECT SYNTAX AS SHOWN GIVEN BELOW
     [
     'pa.Check.between(min_value, max_value, include_min=True, include_max=True, **kwargs)',
     'pa.Check.eq(value, **kwargs)',
@@ -32,7 +33,8 @@ Follow this process:
     'pa.Check.str_startswith(string, **kwargs)',
     'pa.Check.unique_values_eq(values, **kwargs)'
     ]
-    ALSO DONT USE REGEX FOR VALIDATIONS
+    
+
 3. Ensure that each rule specifies the expected data type and applies necessary checks such as:
       name argument should be a valid column name. DO NOT USE ANYOTHER PANDERA 
    - **Data Type Validation** (e.g., `pa.Column(int, nullable=False, name="age")` ensures integers)