tidyspss 0.2.0

A Python package for quick processing and transforming SPSS files

TidySPSS

A Python package for quick processing, transforming, and managing SPSS (.sav) files with support for Excel and CSV inputs. This package is built on top of pyreadstat and pandas to give you flexible, production-ready template for processing and transforming data files into SPSS format with full metadata control.

Philosophy

"Make simple things simple, and complex things possible"

📄 Processing Flow

LOAD → TRANSFORM → CONFIGURE → SAVE

1. LOAD: Read file with metadata preservation
2. TRANSFORM: Apply any pandas operations directly
3. CONFIGURE: Set SPSS-specific options
4. SAVE: Output with all configurations applied

Features

• 📁 Multi-format support: Read from SPSS (.sav/.zsav), Excel (.xlsx/.xls), and CSV files
• 🔄 Comprehensive transformations: Reorder, rename, drop, and keep columns with ease
• 🏷️ Metadata management: Full support for SPSS labels, formats, measures, and display widths
• 🔧 Value replacement: Replace specific values across columns
• 📊 Column positioning: Advanced column reordering with range specifications
• 🔀 File merging: Combine multiple data files by stacking rows with source tracking
• 🌐 Encoding support: Automatic handling of multiple character encodings
• 🔧 Production-ready: Comprehensive logging and error handling

Installation

Install using pip:

pip install tidyspss

Or using uv:

uv add tidyspss

Quick Start

Basic Usage

from tidyspss import read_input_file, process_and_save

# Read a file (automatically detects format)
df, meta = read_input_file("data.sav")  # or .xlsx, .csv

# Process and save with transformations
df, meta = process_and_save(
    df=df,
    meta=meta,
    output_path="output.sav",
    user_variable_rename={"old_name": "new_name"},
    user_variable_drop=["unwanted_col1", "unwanted_col2"],
    user_column_labels={"Q1": "Question 1", "Q2": "Question 2"}
)

Merging Multiple Files

from tidyspss import add_cases, process_and_save

# Merge multiple files by stacking rows
files = ["wave1.sav", "wave2.xlsx", "wave3.csv"]
merged_df, merged_meta = add_cases(
    input_files=files,
    meta_priority=1,  # Use first file's metadata as base
    source_name="source_file"  # Column name for tracking source
)

# The merged dataframe will have a 'source_file' column 
# containing the filename each row came from

# Process and save the merged data
merged_df, merged_meta = process_and_save(
    df=merged_df,
    meta=merged_meta,
    output_path="merged_output.sav"
)

API Reference

Main Functions

read_input_file(file_path)

Reads a file into a pandas DataFrame with metadata.

• Supports: .sav, .zsav, .xlsx, .xls, .csv
• Returns: (DataFrame, metadata) tuple

add_cases(input_files, meta_priority=1, source_name="mrgsrc")

Merges multiple data files by stacking rows (concatenating).

Parameters:

• input_files: List of file paths to merge (can be .sav, .zsav, .xlsx, .xls, or .csv)
• meta_priority: Which file's metadata to use as base
  • If int: 1-based index of the file in input_files list
  • If str: exact filename that exists in input_files list
• source_name: Column name for tracking source file of each record (default: "mrgsrc")

Returns:

• (merged_df, merged_meta): Tuple of concatenated DataFrame with source tracking column and consolidated metadata

Example:

# Use first file's metadata
df, meta = add_cases(["data1.sav", "data2.xlsx"], meta_priority=1)

# Use specific file's metadata
df, meta = add_cases(["data1.sav", "data2.xlsx"], meta_priority="data2.xlsx")

# Custom source column name
df, meta = add_cases(files, source_name="wave_source")

process_and_save(df, meta, output_path, **kwargs)

Processes DataFrame with configurations and saves to SPSS format.

Parameters:

• df: Input DataFrame
• meta: Metadata from SPSS file (or None)
• output_path: Path for output .sav file
• user_column_position: Dict for column reordering
• user_variable_drop: List of columns to drop
• user_variable_keep: List of columns to keep (drops all others)
• user_variable_rename: Dict for renaming columns
• user_value_replacement: Dict for replacing values
• user_column_labels: Dict of column labels
• user_variable_value_labels: Dict of value labels
• user_variable_format: Dict of variable formats
• user_variable_measure: Dict of variable measures
• user_variable_display_width: Dict of display widths
• user_missing_ranges: Dict of missing value ranges
• user_note: File note string
• user_file_label: File label string
• user_compress: Boolean for file compression
• user_row_compress: Boolean for row compression

Requirements

• Python ≥ 3.11
• pandas ≥ 2.3.0
• pyreadstat ≥ 1.3.0
• openpyxl ≥ 3.0.0

License

MIT License - see LICENSE file for details.