metaprinter 0.2.9

print and extract spss metadata

SPSS Metadata Printer 📊

Easy-to-use Python package for extracting, viewing, and exporting metadata from SPSS files with beautiful formatting.

✨ Features

• 📋 Pretty-print comprehensive SPSS metadata to console
• 💾 Export metadata summaries to text files automatically saved to Downloads
• 📄 Extract metadata dictionary to JSON for programmatic access and archival
• 🏷️ Create label mappings from Excel for structured variable labeling
• 📊 Detailed variable information including labels, types, and value mappings
• 🎨 Beautiful table formatting with configurable width and display options

🚀 Quick Start

Installation

pip install metaprinter

Or using uv:

uv add metaprinter

Basic Usage

from metaprinter import print_metadata, export_metadata, extract_metadict, make_labels
import pyreadstat

# Load your SPSS file
df, meta = pyreadstat.read_sav('data.sav')

# Display beautiful metadata summary inside a notebook
print_summary = print_metadata(df, meta)

# Export to Downloads/metadata_summary.txt
export_summary = export_metadata(df, meta)

# Extract metadata to JSON (Downloads/meta_dictionary.json)
extract_metadict(meta)

# Create label mappings from Excel
col_labels, val_labels = make_labels("label_mappings.xlsx")

Output Preview:

============================================================
SPSS FILE METADATA
============================================================
File encoding   : 'UTF-8'
Number of cols  : 25
Number of rows  : 100
Table name      : 'Table'
File label      : 'Customer Satisfaction Survey'
Notes           : 'Notes'

VARIABLE METADATA
============================================================
┌───────────────┬─────────┬──────────┬───────────┬──────────────┬─────────────────────┬─────────────────────┐
│ column        ┆ dtype   ┆ column_n ┆ n_uniques ┆ n_categories ┆ column_label        ┆ value_labels        │
│ ---           ┆ ---     ┆ ---      ┆ ---       ┆ ---          ┆ ---                 ┆ ---                 │
│ str           ┆ str     ┆ i64      ┆ i64       ┆ i64          ┆ str                 ┆ str                 │
╞═══════════════╪═════════╪══════════╪═══════════╪══════════════╪═════════════════════╪═════════════════════╡
│ respondent_id ┆ Int64   ┆ 1547     ┆ 1547      ┆ 0            ┆ Respondent ID       ┆                     │
│ satisfaction  ┆ Int64   ┆ 1523     ┆ 5         ┆ 5            ┆ Satisfaction Level  ┆ {                   │
│               ┆         ┆          ┆           ┆              ┆                     ┆   "1": "Very Low",  │
│               ┆         ┆          ┆           ┆              ┆                     ┆   "2": "Low",       │
│               ┆         ┆          ┆           ┆              ┆                     ┆   "3": "Neutral",   │
│               ┆         ┆          ┆           ┆              ┆                     ┆   "4": "High",      │
│               ┆         ┆          ┆           ┆              ┆                     ┆   "5": "Very High"  │
│               ┆         ┆          ┆           ┆              ┆                     ┆ }                   │
│ age           ┆ Int64   ┆ 1534     ┆ 6         ┆ 6            ┆ Age Group Category  ┆ {                   │
│               ┆         ┆          ┆           ┆              ┆                     ┆   "1": "18-25",     │
│               ┆         ┆          ┆           ┆              ┆                     ┆   "2": "26-35",     │
│               ┆         ┆          ┆           ┆              ┆                     ┆   "3": "36-45",     │
│               ┆         ┆          ┆           ┆              ┆                     ┆   "4": "46-55",     │
│               ┆         ┆          ┆           ┆              ┆                     ┆   "5": "56-65",     │
│               ┆         ┆          ┆           ┆              ┆                     ┆   "6": "65+"        │
│               ┆         ┆          ┆           ┆              ┆                     ┆ }                   │
│ ...           ┆ ...     ┆ ...      ┆ ...       ┆ ...          ┆ ...                 ┆ ...                 │
└───────────────┴─────────┴──────────┴───────────┴──────────────┴─────────────────────┴─────────────────────┘

📖 API Reference

print_metadata(df, meta, show_all_columns=True, max_width=222, include_all=False)

Print a comprehensive metadata summary for SPSS data loaded with pyreadstat.

Parameters:

• df: DataFrame containing the SPSS data (Pandas or Polars)
• meta: Metadata object from pyreadstat.read_sav()
• show_all_columns: Whether to show all columns without truncation (default: True, optional)
• max_width: Maximum table width in characters (default: 222, optional)
• include_all: Whether to include all available metadata fields (default: False, optional)

---

export_metadata(df, meta, filename=None, show_all_columns=True, max_width=222, include_all=False)

Export SPSS metadata summary to a text file in the Downloads folder.

Parameters:

• df: DataFrame containing the SPSS data (Pandas or Polars)
• meta: Metadata object from pyreadstat.read_sav()
• filename: Custom filename without extension (default: "metadata_summary")
• show_all_columns: Whether to show all columns without truncation (default: True, optional)
• max_width: Maximum table width in characters (default: 222, optional)
• include_all: Whether to include all available metadata fields (default: False, optional)

---

extract_metadict(meta, include_all=False, output_path=None)

Extract metadata dictionary from pyreadstat meta object and save as JSON.

Parameters:

• meta: Metadata object from pyreadstat.read_sav()
• include_all: Whether to include all metadata fields or just essential ones (default: False, optional)
• output_path: Custom file path for JSON output (must end with .json). If None, saves to Downloads/meta_dictionary.json (default: None, optional)

Example JSON Output (basic):

{
    "General Information": {
        "Notes": "Survey conducted in 2024",
        "Creation Time": "2024-01-15 10:30:00",
        "File Encoding": "UTF-8",
        "Number of Columns": 25,
        "Number of Rows": 100,
        "Table Name": "Table",
        "File Label": "Customer Satisfaction Survey"
    },
    "Variable Information": {
        "Column Names to Labels": {
            "respondent_id": "Respondent ID",
            "satisfaction": "Satisfaction Level",
            "age": "Age Group Category"
        },
        "Variable Value Labels": {
            "satisfaction": {
                "1": "Very Low",
                "2": "Low",
                "3": "Neutral",
                "4": "High",
                "5": "Very High"
            }
        }
    }
}

---

make_labels(input_path, output_path=None, ...)

Transform Excel file with label mappings into Python dictionaries for column labels and value labels.

Parameters:

• input_path (str): Path to Excel file containing label mappings
• output_path (str, optional): Path for output Python file. If None, no file is created (default: None)
• col_label_sheet (str, optional): Name of sheet with column labels (default: "col_label")
• value_label_sheet (str, optional): Name of sheet with value labels (default: "value_label")
• col_dict_name (str, optional): Name for column labels dictionary in output (default: "user_column_labels")
• value_dict_name (str, optional): Name for value labels dictionary in output (default: "user_variable_value_labels")
• col_quote_style (str, optional): Quote style for column labels (default: ''')
• value_quote_style (str, optional): Quote style for value labels (default: ''')
• col_variable_column (str, optional): Variable name column in col_label sheet (default: "variable")
• col_label_column (str, optional): Label column in col_label sheet (default: "label")
• value_variable_column (str, optional): Variable name column in value_label sheet (default: "variable")
• value_value_column (str, optional): Value/code column in value_label sheet (default: "value")
• value_label_column (str, optional): Label column in value_label sheet (default: "label")
• verbose (bool, optional): Print progress messages (default: True)

Returns:

• Tuple of (column_labels_dict, value_labels_dict)

Excel Structure:

Your Excel file should have two sheets:

1. Column Labels Sheet (default name: "col_label"):

   variable	label
   age	Age of respondent
   gender	Gender
   income	Annual household income

2. Value Labels Sheet (default name: "value_label"):

   variable	value	label
   gender	1	Male
   gender	2	Female
   income	1	Under $25k
   income	2	$25k-$50k

Usage Examples:

from metaprinter import make_labels

# Basic usage - creates Python file and returns dictionaries
col_labels, val_labels = make_labels(
    input_path="survey_labels.xlsx",
    output_path="survey_labels.py"
)

# Return only (no file output) - perfect for notebooks
col_labels, val_labels = make_labels("survey_labels.xlsx")

Output File Example (when output_path is provided):

user_column_labels = {
    'age': '''Age of respondent''',
    'gender': '''Gender''',
    'income': '''Annual household income''',
}


user_variable_value_labels = {
    'gender': {
        1: 'Male',
        2: 'Female',
    },
    'income': {
        1: 'Under $25k',
        2: '$25k-$50k',
    },
}

📋 Requirements

• Python >=3.11
• pyreadstat >=1.3.0
• polars >=1.3.0
• pandas >=2.3.0

📝 License

MIT License - see LICENSE file for details

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.