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Overview

The fmt_fecha() function provides robust date formatting for invoice processing, converting various date input formats into a standardized string format. It handles invalid dates gracefully and uses day-first parsing to accommodate Latin American date conventions.

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Function Signature

def fmt_fecha(valor, formato="%d/%m/%Y")
Source: main.py:25-34

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Parameters

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valor
any
required
The date value to format. Can be:
  • String date representation (e.g., “15/03/2026”, “2026-03-15”)
  • Pandas datetime object
  • Python datetime object
  • Empty string or NaN value
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formato
string
default:"%d/%m/%Y"
The output date format using Python strftime codes. Common formats:
  • "%d/%m/%Y" → “15/03/2026” (default)
  • "%Y-%m-%d" → “2026-03-15”
  • "%d-%m-%Y" → “15-03-2026”
  • "%B %d, %Y" → “March 15, 2026”

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Return Value

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result
string | None
Returns the formatted date string according to the specified format. Returns None if the input is empty or NaN. Returns the original valor unchanged if parsing fails.

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Date Parsing Behavior

The function uses dayfirst=True when parsing dates with pd.to_datetime(). This means ambiguous dates like “03/04/2026” are interpreted as April 3rd, 2026 (not March 4th), following Latin American date conventions.

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Parsing Logic

  1. Empty/NaN Check: If valor is NaN or empty string, returns None immediately
  2. Date Parsing: Attempts to parse using pd.to_datetime() with dayfirst=True and errors="coerce"
  3. Validation: If parsing results in NaT (Not a Time), returns original valor unchanged
  4. Formatting: If successful, formats the datetime using the specified formato
  5. Error Handling: If any exception occurs, returns original valor unchanged

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Error Handling

The function includes multiple layers of error protection:

Null/Empty Values

Returns None for NaN or empty strings without attempting parsing

Invalid Dates

Returns original value if date cannot be parsed (e.g., “invalid-date”)

Exception Safety

Catches all exceptions and returns original value to prevent crashes

Coerce Errors

Uses errors="coerce" to convert unparseable dates to NaT gracefully

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Examples

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Basic Usage

# Standard date formatting (day/month/year)
fmt_fecha("15/03/2026")
# Returns: "15/03/2026"

# ISO format input
fmt_fecha("2026-03-15")
# Returns: "15/03/2026"

# Custom output format
fmt_fecha("2026-03-15", formato="%Y-%m-%d")
# Returns: "2026-03-15"

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Day-First Parsing

# Ambiguous date: interpreted as day/month/year
fmt_fecha("03/04/2026")
# Returns: "03/04/2026" (April 3rd, not March 4th)

# Unambiguous date
fmt_fecha("25/12/2025")
# Returns: "25/12/2025" (December 25th)

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Error Handling

# Empty or NaN values
fmt_fecha("")
# Returns: None

fmt_fecha(pd.NA)
# Returns: None

# Invalid date string
fmt_fecha("not-a-date")
# Returns: "not-a-date" (unchanged)

# Partially valid date
fmt_fecha("32/13/2026")  # Invalid day and month
# Returns: "32/13/2026" (unchanged)

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Invoice Processing Context

# As used in the invoice transformation pipeline (main.py:75-77)
fecha_emision = fmt_fecha(row["Fecha Emision"])
# Converts Excel date to "dd/mm/yyyy" format

fecha_vencimiento = fmt_fecha(row["Fecha Vencimiento"])
# Formats due date

fecha_pago = fmt_fecha(row["Fecha Pago"], "%d/%m/%Y")
# Formats payment date with explicit format

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Common Use Cases

Input TypeExample InputOutput (Default)Notes
String (DMY)“15/03/2026""15/03/2026”Direct passthrough if already formatted
String (ISO)“2026-03-15""15/03/2026”Converted to day-first format
Pandas Timestamppd.Timestamp("2026-03-15")”15/03/2026”Formatted from datetime object
Excel Serial46095”15/03/2026”Pandas auto-converts Excel date serials
Empty/NaN"" or pd.NANoneNull-safe handling
Invalid”invalid""invalid”Returns original value
When working with dates from Excel files, be aware that Excel may store dates as serial numbers. Pandas read_excel() automatically converts these to datetime objects, which fmt_fecha() can then format correctly.

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Technical Details

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Dependencies

  • pandas: For robust date parsing with pd.to_datetime() and null handling with pd.isna()
  • datetime: For strftime formatting codes

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Performance Considerations

  • The function uses errors="coerce" which is safer but slightly slower than errors="raise"
  • For bulk processing of thousands of dates, consider vectorizing with pandas Series operations instead of row-by-row calls

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Integration Points

This utility is called from:
  • transformar_fila() function (main.py:74) - Used to format invoice dates before JSON transformation
  • Invoice document emission dates: IdentificacionDocumento.FechaEmision
  • Payment due dates: IdentificacionDocumento.FechaVencimiento
  • Payment dates: FormasPago[].Fecha

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