filemapper 1.0.2

A CLI tool for mapping and transforming CSV files based on YAML configuration

FileMapper

A CSV transformation tool controlled by YAML configuration. Use this tool to transform, validate, map, and convert CSV data without writing code.

Core Capabilities

• Column mapping: Rename, copy, or combine columns
• Data validation: Type checking, regex patterns, numeric ranges, required fields
• Transformations: Text case, trimming, whitespace removal
• Type conversion: String ↔ integer, float, boolean, date
• Conditional logic: If-then rules with multiple conditions
• Expressions: Arithmetic, comparisons, boolean logic
• String matching: contains and icontains (case-insensitive) operators
• Cross-referencing: Later mappings can reference already-mapped output columns

Installation

From PyPi

pip install -U filemapper

Command Reference

Basic Usage

# Transform CSV with configuration
filemapper -i input.csv -c config.yaml -o output.csv

# Validate input data only
filemapper -i input.csv -c config.yaml --validate-only

# Strict mode (fail on first error)
filemapper -i input.csv -c config.yaml -o output.csv --strict

All Command Options

Option	Required	Default	Description
-i, --input FILE	Yes	-	Input CSV file path
-c, --config FILE	Yes	-	YAML configuration file path
-o, --output FILE	Conditional	-	Output CSV file path
--validate-only	No	false	Validate input against schema, then exit
--version	No	-	Print the version number
--strict	No	false	Stop processing on first validation/map error
--encoding ENCODING	No	utf-8	Character encoding for all files
--delimiter CHAR	No	,	CSV delimiter character

--output is required unless using --validate-only

YAML Configuration Structure

Every configuration file requires exactly four top-level keys: version, input, output, mappings.

Configuration Skeleton

version: "1.0"

input:
  columns:
    - name: column_name
      type: string              # string | integer | float | boolean | date
      required: true            # optional, default: false
      pattern: "regex"          # optional, regex validation
      min: 0                    # optional, numeric minimum
      max: 100                  # optional, numeric maximum

output:
  columns:
    - name: output_column
      type: string

mappings:
  - output: output_column
    source: column_name         # Use ONE of: source, value, expression, or conditions

Input Schema Definition

Define expected columns with validation rules:

input:
  columns:
    - name: age                 # Column name in input CSV
      type: integer             # Data type
      required: true            # Validation will fail if missing
      min: 0                    # Numeric range validation
      max: 150
    - name: email
      type: string
      required: true
      pattern: "^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$"  # Regex validation

Validation types: string, integer, float, boolean, date

Optional validation rules:

• required: true/false - Field must be present
• pattern: "regex" - String must match regex
• min: number - Numeric minimum value
• max: number - Numeric maximum value

Output Schema Definition

Define output columns and their order:

output:
  columns:
    - name: fullName           # Column name in output CSV
      type: string             # Expected output type
    - name: ageGroup
      type: string

Columns appear in output CSV in the order listed here.

Mapping Rules Reference

Each mapping rule produces one output column. Exactly one of these four sources is required: source, value, expression, or conditions.

1. Source Mapping (Column Copy/Rename)

Copy a column directly, optionally renaming it:

- output: firstName
  source: first_name

2. Value Templates (String Building)

Build strings using {field} placeholders. Placeholders are replaced with input column values:

# Combine multiple fields
- output: fullName
  value: "{first_name} {last_name}"

# Static string
- output: status
  value: "Active"

# Mix literals and fields
- output: greeting
  value: "Hello, {first_name}!"

3. Expressions (Calculations)

Evaluate Python-like expressions with {field} references:

# Boolean expression
- output: isAdult
  expression: "{age} >= 18"

# Arithmetic
- output: total
  expression: "{price} * {quantity}"

# Complex logic
- output: eligible
  expression: "{age} >= 18 and {score} > 50"

Supported operators:

• Arithmetic: +, -, *, /, // (floor division), % (modulo), ** (power)
• Comparison: ==, !=, <, <=, >, >=
• Logical: and, or, not
• String matching:
  • contains - case-sensitive substring check: "{name} contains 'Smith'"
  • icontains - case-insensitive substring check: "{os} icontains 'windows'"

Numeric strings are auto-converted for arithmetic. Expressions are security-validated (no function calls, imports, or attribute access allowed).

4. Conditional Logic

First matching when condition wins. Conditions support both value (template strings) and expression (calculations):

# Static values based on conditions
- output: ageGroup
  conditions:
    - when: "{age} < 18"
      value: "Minor"
    - when: "{age} >= 18 and {age} < 65"
      value: "Adult"
    - when: "{age} >= 65"
      value: "Senior"
    - default: "Unknown"

# Dynamic values (select different columns based on condition)
- output: primaryOS
  conditions:
    - when: "{X} == ''"
      value: "{Y}"              # Use Y if X is empty
    - default: "{X}"            # Otherwise use X

# String matching
- output: platform
  conditions:
    - when: "{os} contains 'Windows'" # Case sensitive matching
      value: "Windows"
    - when: "{os} icontains 'linux'" # Case insensitive matching
      value: "Linux"
    - default: "Other"

# Expressions in conditions (for calculated outputs)
- output: priorityScore
  conditions:
    - when: "{score} < 10"
      value: "Low"
    - when: "{score} >= 10"
      expression: "{score} * 3"   # Calculate value when condition matches

Important: Each condition can have either value OR expression, not both. Use value for static or template strings, expression for calculations.

5. Text Transformations

Apply after value resolution, before type conversion:

- output: email
  source: email_address
  transform: lowercase

Available transforms:

• uppercase - Convert to UPPERCASE
• lowercase - Convert to lowercase
• trim - Remove leading/trailing whitespace
• titlecase - Convert To Title Case
• strip_whitespace - Remove all whitespace

6. Type Conversions

Convert between data types with optional format strings:

# String to date
- output: registrationDate
  source: signup_date
  type_conversion:
    from: string
    to: date
    format: "%Y-%m-%d"

# Date to string
- output: formattedDate
  source: date_field
  type_conversion:
    from: date
    to: string
    format: "%d/%m/%Y"

# String to boolean
- output: isActive
  source: active_flag
  type_conversion:
    from: string
    to: boolean

Supported conversions:

From	To	Notes
string	integer	Must be valid integer
string	float	Must be valid number
string	boolean	Accepts: true/false, 1/0, yes/no, y/n, on/off (case-insensitive)
string	date	Requires format parameter (e.g., "%Y-%m-%d")
integer	string	Direct conversion
integer	float	Direct conversion
float	string	Direct conversion
float	integer	Truncates decimal
date	string	Requires format parameter
boolean	string	Outputs "True" or "False"

7. Default Values

Provide fallback when source/value is empty or None. Supports {field} templates:

# Static default
- output: region
  source: country_code
  default: "UNKNOWN"
  transform: uppercase

# Dynamic default (use another field)
- output: displayName
  source: nickname
  default: "{first_name}"

8. Cross-Referencing Output Columns

Later mappings can reference already-mapped output columns using {output_column_name}:

mappings:
  - output: fullName
    value: "{first_name} {last_name}"

  - output: greeting
    value: "Welcome, {fullName}!"    # References the fullName output column

This enables multi-stage transformations where one mapping builds on another.

Mapping Rule Processing Order

Within a single mapping rule, operations apply in this sequence:

1. Resolve value (source / value / expression / conditions)
2. Apply default if value is empty (with {field} template resolution)
3. Apply text transformation
4. Apply type conversion

Template Placeholder Rules

Use {field_name} to reference columns in:

• value fields
• expression fields
• conditions.value fields
• default fields

Important: {field_name} can reference:

• Input columns (from CSV)
• Already-mapped output columns (earlier in mappings list)

Example

The examples/ directory contains the following files:

Input file: examples/input.csv Configuration file: examples/config.yaml Output file: examples/output.csv

The output file was created by running this command: python filemapper.py -i ./examples/input.csv -c ./examples/config.yaml -o ./examples/output.csv

Error Handling and Exit Codes

Error Modes

Lenient mode (default): Collects all errors and continues processing

python3 filemapper.py -i input.csv -c config.yaml -o output.csv

• Validation errors are reported but processing continues
• Mapping errors are reported but successfully mapped rows are written
• Exit code 0 if any rows succeed, 1 if catastrophic failure

Strict mode: Stops on first error

python3 filemapper.py -i input.csv -c config.yaml -o output.csv --strict

• Stops immediately on first validation error
• Stops immediately on first mapping error
• Exit code 1 on any error

Exit Codes

Code	Meaning
0	Success
1	Error (validation or mapping)
130	Keyboard interrupt (Ctrl+C)

Error Output Format

Validation errors show:

• Row number
• Column name
• Validation rule violated
• Actual value received

Example:

✗ Validation failed with 2 error(s):
  - Row 5: Column 'age': Value '200' exceeds maximum (150)
  - Row 8: Column 'email': Value 'invalid' does not match pattern

Mapping errors show:

• Row number
• Output column being mapped
• Error description

Example:

✗ Mapping failed with 1 error(s):
  - Row 3: Failed to map 'registrationDate': time data '2024-13-45' does not match format '%Y-%m-%d'

Quick Reference for AI Agents

Decision Tree: Which Mapping Type?

Need to transform CSV data?
│
├─ Copy single column? → Use `source`
│  └─ Example: `source: first_name`
│
├─ Combine multiple columns or add text? → Use `value` template
│  └─ Example: `value: "{first_name} {last_name}"`
│
├─ Calculate or evaluate expression? → Use `expression`
│  └─ Example: `expression: "{price} * {quantity}"`
│
└─ Different outputs based on conditions? → Use `conditions`
   └─ Example:
      conditions:
        - when: "{age} < 18"
          value: "Minor"
        - default: "Adult"

Common Patterns

Pattern 1: Rename column

- output: newName
  source: oldName

Pattern 2: Normalise text

- output: email
  source: emailAddress
  transform: lowercase

Pattern 3: Fill missing values

- output: country
  source: country_code
  default: "US"

Pattern 4: Categorise numeric values

- output: priceRange
  conditions:
    - when: "{price} < 10"
      value: "Budget"
    - when: "{price} < 50"
      value: "Mid-range"
    - default: "Premium"

Pattern 5: Combine and transform

- output: fullName
  value: "{first_name} {last_name}"
  transform: titlecase

Pattern 6: Convert date formats

- output: formattedDate
  source: raw_date
  type_conversion:
    from: string
    to: date
    format: "%Y-%m-%d"

Pattern 7: Check if string contains substring

- output: osType
  conditions:
    - when: "{operating_system} contains 'Windows'" # Case sensitive matching
      value: "Windows"
    - when: "{operating_system} icontains 'linux'" # Case insensitive matching
      value: "Linux"
    - default: "Other"

Pattern 8: Reference previously mapped output

- output: firstName
  source: first_name

- output: greeting
  value: "Hello, {firstName}!"  # References mapped output, not input

Pattern 9: Conditional with calculated values

- output: adjustedScore
  conditions:
    - when: "{score} < 10"
      value: "Low"
    - when: "{score} >= 10 and {score} < 100"
      expression: "{score} * 2"    # Calculate when in range
    - when: "{score} >= 100"
      expression: "{score} * 1.5"  # Different calculation for high scores