AutoDataPreprocess 0.1.4

A high-level library for automatic preprocessing of tabular data

AutoDataPreprocess

AutoDataPreprocess is a comprehensive Python library for automated data preprocessing. It provides a wide range of tools and techniques to clean, transform, and prepare data for machine learning models.

Features

• Data loading from various sources (CSV, JSON, Excel, HTML, XML, Pickle, SQL, API)
• Basic data analysis and visualization
• Data cleaning (handling missing values, outliers, duplicates)
• Feature engineering
• Encoding of categorical variables (Onehot, label, ordinal, target, woe, james_stein, catboost, binary)
• Scaling and normalization
• Dimensionality reduction
• Feature selection
• Handling imbalanced data
• Time series preprocessing
• Data anonymization

Installation

You can install AutoDataPreprocess using pip: pip install autodatapreprocess

Quick Start

from autodatapreprocess import AutoDataPreprocess

# Load data
adp = AutoDataPreprocess('your_data_file.csv')

# Perform basic analysis
adp.basic_analysis()

# Clean the data
cleaned_data = adp.clean(missing='mean', outliers='iqr')

# Perform feature engineering
engineered_data = adp.fe(target_column='target', polynomial_degree=2)

# Encode categorical variables
encoded_data = adp.encode(methods={'category_column': 'onehot'})

# Scale the data
scaled_data = adp.scale(method='standard')

Detailed Usage

Data Loading

Load data from various sources:

# From CSV
adp = AutoDataPreprocess('data.csv')

# From SQL
adp = AutoDataPreprocess(sql_query="SELECT * FROM table", sql_connection_string="your_connection_string")

# From API
adp = AutoDataPreprocess(api_url="https://api.example.com/data", api_params={"key": "value"})

Data Cleaning

Clean your data with various options:

cleaned_data = adp.clean(
    missing='mean',
    outliers='iqr',
    drop_threshold=0.7,
    date_format='%Y-%m-%d',
    remove_duplicates=True
)

Feature Engineering

Perform feature engineering:

engineered_data = adp.fe(
    target_column='target',
    polynomial_degree=2,
    interaction_only=False,
    bin_numeric=True,
    num_bins=5,
    cyclical_features=['month', 'day_of_week'],
    text_columns=['description'],
    date_columns=['date']
)

Encoding

Encode categorical variables:

encoded_data = adp.encode(
    methods={
        'category1': 'onehot',
        'category2': 'label',
        'category3': 'target'
    },
    target_column='target'
)

Scaling and Normalization

Scale or normalize your data:

scaled_data = adp.scale(method='standard')
normalized_data = adp.normalize(method='l2')

Dimensionality Reduction

Reduce the dimensionality of your data:

reduced_data = adp.dimreduction(method='pca', n_components=5)

Feature Selection

Select the most important features:

selected_data = adp.feature_selection(
    target_column='target',
    method='correlation',
    correlation_threshold=0.8
)

Handling Imbalanced Data

Balance your dataset:

balanced_data = adp.balance_data(
    target_column='target',
    method='smote',
    sampling_strategy='auto'
)

Time Series Preprocessing

Preprocess time series data:

preprocessed_ts_data = adp.time_series_preprocessing(
    time_column='date',
    freq='D',
    method='mean',
    detrend_columns=['value'],
    seasonality_columns=['value'],
    lag_columns=['value'],
    lags=[1, 7, 30]
)

Data Anonymization

Anonymize sensitive data:

anonymized_data = adp.apply_anonymization(
    columns=['sensitive_column'],
    method='hash',
    hash_algorithm='sha256'
)