wlibrary 1.1.1

Python library for intelligent Excel file processing with automatic data cleaning and type detection

wlibrary

Python library for intelligent Excel file processing with automatic data cleaning and type detection.

import wlibrary as w

df = w.read("data.xlsx")
df = w.clean(df)
w.save(df, "clean.json")

Installation

pip install wlibrary

Quick Start

import wlibrary as w

# Read Excel file
df = w.read("file.xlsx")

# Clean and normalize data
df = w.clean(df)

# Save to any format
w.save(df, "output.json")

Features

• Smart Reading: Automatically handles merged cells and complex structures
• Extended Type Detection: Detects emails, phone numbers, URLs, currency, coordinates, and more
• Quality Analysis: Provides quality scores and identifies data issues
• Auto-Cleaning: Normalizes column names, removes empty rows, cleans whitespace
• Fast Performance: 3x faster with built-in caching
• Memory Efficient: 30% memory reduction with automatic optimization

Core Functions

Reading Files

w.read("file.xlsx")              # Read Excel file with caching
w.sheets("file.xlsx")            # Get list of sheet names
w.preview("file.xlsx", rows=10)  # Preview first N rows
w.info("file.xlsx")              # Get file information

Data Analysis

w.types(df, extended=True)       # Detect column types
w.analyze(df)                    # Full structure analysis
w.suggest(df)                    # Get improvement suggestions
w.dups(df)                       # Find duplicate rows

Data Cleaning

w.clean(df)                      # Complete cleaning pipeline
w.normalize(df)                  # Normalize column names only

Exporting Data

w.save(df, "output.json")        # JSON
w.save(df, "output.csv")         # CSV
w.save(df, "output.xlsx")        # Excel

Quick Operations

w.quick("file.xlsx")             # Generate quality report
w.pipeline("file.xlsx")          # Complete processing pipeline

Type Detection

Automatically detects 17+ data types:

Basic Types:

• numeric, date, categorical, text, boolean, id

Extended Types:

• email, phone, url, uuid, ipv4, currency, coordinate, postal_code, address, json

Example:

types = w.types(df, extended=True)
for col, info in types.items():
    print(f"{col}: {info['type']} (confidence: {info['confidence']:.0%})")

Quality Analysis

Get comprehensive quality metrics:

info = w.analyze(df)
print(f"Quality Score: {info['quality_metrics']['score']}/100")
print(f"Completeness: {info['quality_metrics']['avg_completeness']:.0%}")

Automatically detects issues:

• Duplicate rows
• High null rates (>50% missing)
• Outliers (>3 IQR)
• Single-value columns
• Inconsistent formats

Examples

Basic Cleaning

import wlibrary as w

df = w.read("messy_data.xlsx")
df = w.clean(df)
w.save(df, "cleaned_data.xlsx")

Quality Report

import wlibrary as w

report = w.quick("data.xlsx")
print(report)

Output:

FILE: data.xlsx
Quality: 85/100
Issues: 3
  - duplicates: 5 rows (5%)
  - high_nulls: column1 (60%)
Suggestions:
  - [HIGH] Remove duplicate rows
  - [MEDIUM] Fill or remove high-null columns

Finding Specific Data

import wlibrary as w

df = w.read("contacts.xlsx")
types = w.types(df, extended=True)

# Find email columns
email_cols = [col for col, info in types.items() if info['type'] == 'email']
print(f"Email columns: {email_cols}")

Batch Processing

import wlibrary as w
from pathlib import Path

for file in Path("data").glob("*.xlsx"):
    result = w.pipeline(str(file))
    print(f"{file.name}: Quality {result['score']}/100")
    w.save(result['df'], f"clean/{file.stem}.json")

Performance

Built-in caching makes repeat operations 3x faster:

# First read: normal speed
df = w.read("large_file.xlsx")  # 2.5 seconds

# Cached read: very fast
df = w.read("large_file.xlsx")  # 0.1 seconds

Memory optimization reduces usage by 30%:

df = w.read("file.xlsx", optimize=True)  # Automatically downcast types

Cache Management

w.cache()        # Show cache info
w.clear()        # Clear cache

Configuration

Customize behavior:

from wlibrary.config import get_config, set_config

config = get_config()
config.performance.max_workers = 8
config.cleaner.empty_row_threshold = 0.7
set_config(config)

Or use a config file:

{
  "performance": {
    "enable_cache": true,
    "max_workers": 4
  },
  "extended_types": {
    "detect_currency": true,
    "detect_email": true
  }
}

from wlibrary.config import load_config
load_config("config.json")

Smart Reading

Automatically detect complex Excel structures:

structure = w.smart("complex_file.xlsx")

# Access different parts
print(structure.metadata)      # {'project': 'Name', 'client': 'ACME'}
print(structure.categories)    # ['Category1', 'Category2']
print(structure.table_data)    # Clean DataFrame

Or just get the clean table:

df = w.smart_df("complex_file.xlsx")

Help

Built-in documentation:

import wlibrary as w
w.help()

Requirements

• Python 3.10+
• pandas
• openpyxl
• numpy

License

MIT License