dataglass 0.8.1

dataglass is a Python library for data preprocessing, exploratory data analysis (EDA), and machine learning. It includes modules for handling missing values, detecting and resolving duplicates, managing outliers, feature encoding, type conversion, scaling, and pipeline integration. With its latest update, dataglass introduces intelligent automation which dynamically adapting preprocessing steps based on dataset characteristics, minimizing manual configuration and accelerating your workflow.

🔮 dataglass

A modular and lightweight library for preprocessing, analysis, and modeling structured datasets in Python.

dataglass provides an easy-to-use yet powerful framework to handle essential preprocessing tasks such as missing value handling, duplicate removal, outlier detection and management, feature encoding, type conversion, and feature scaling — all designed to integrate with custom pipeline workflows. dataglass introduces intelligent automation which dynamically adapting preprocessing steps based on dataset characteristics, minimizing manual configuration and accelerating your workflow.

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🤖 Auto-Preprocessing (New!)

dataglass now features an intelligent auto-preprocessing module that dynamically constructs the optimal pipeline based on your dataset’s characteristics, so no manual configuration required.

Just call a single function:

df_cleaned = dg.auto_preprocess_for_analysis(
    data = df,
    verbose = True      # Show decisions and intermediate steps in a log file
)

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🚀 Preprocessing Features

❓ Missing Value Handling
Drop rows, imputation by datatype (mean, median, mode), imputation by adjacent values (forward/backward fill), and interpolation (linear, time-based)

📑 Duplicate Detection & Removal
Detect and remove exact and fuzzy duplicates using full and partial similarity checks

❗ Outlier Detection & Handling
Detect outliers using IQR, Z-Score, Isolation Forest, and Local Outlier Factor (LOF)
Handle them by dropping, replacing with median, or capping with boundaries
Includes visualization tools: before vs. after boxplots and histograms

🔢 Feature Encoding
Supports label encoding, one-hot encoding, and hashing for categorical variables

🔁 Type Conversion
Automatic datatype inference and user-defined type conversion support

📏 Feature Scaling
Includes Min-Max scaling, Z-Score (standard) scaling, robust scaling, and L2 normalization

🧩 Pipeline Compatibility
Custom lightweight pipeline interface for chaining reusable preprocessing steps

💾 Non-destructive Processing
All operations are applied on copies, and original data remains unchanged

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📦 Installation

pip install dataglass

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📘 Usage Examples (Pipeline vs Functional)

There are two approaches to using the library features: the pipeline architecture and standalone function usage. The examples below demonstrate both methods.

🧩 Pipeline Architecture (Simplest Configuration)

Use this approach when you want a clean, modular, and reusable workflow for end-to-end preprocessing.

# Importing the library and dependencies
import dataglass as dg
import pandas as pd
import numpy as np

# Creating a sample dataframe with a missing value and a categorical column
df = pd.DataFrame({
    "name": ["John", "Jane", "Jack"],
    "age": [40, np.nan, 50],
    "gender": ["male", "female", "male"]
})

# Step 1: Handle missing values by dropping rows that contain any missing value
handle_missing = dg.HandleMissingStep(dg.HandleMissingMethod.DROP)

# Step 2: Handle duplicates by removing exact duplicate rows
handle_duplicate = dg.HandleDuplicateStep(dg.HandleDuplicateMethod.EXACT)

# Step 3: Automatically detect and convert datatypes; verbose=True prints conversion logs
type_conversion = dg.TypeConversionStep(dg.ConvertDatatypeMethod.AUTO, verbose=True)

# Step 4: Detect outliers using IQR and remove them
handle_outlier = dg.HandleOutlierStep(dg.DetectOutlierMethod.IQR, dg.HandleOutlierMethod.DROP)

# Step 5: Scale the 'age' column using Min-Max scaling
scale_feature = dg.ScaleFeatureStep({"column": ["age"], "scaling_method": ["MINMAX_SCALING"]})

# Step 6: Encode the 'gender' column using label encoding
encode_feature = dg.EncodeFeatureStep(dg.FeatureEncodingMethod.LABEL_ENCODING, ["gender"])

# Create the pipeline by chaining all the preprocessing steps in the desired order
dp = dg.DataPipeline([
    handle_missing,
    handle_duplicate,
    type_conversion,
    handle_outlier,
    scale_feature,
    encode_feature,
])

# Apply the pipeline to the dataframe
df_cleaned = dp.apply(df)

# Display the cleaned and transformed dataframe
print(f"Preprocessed Data:\n{df_cleaned}")

# =========== Expected Terminal Output =============

# Before automatic datatype conversion, the datatype are as follows:
# name       object
# age       float64
# gender     object
# dtype: object

# After automatic datatype conversion, the datatype are as follows:
# name      object
# age        int64
# gender    object
# dtype: object

# Preprocessed Data:
#    name  age gender  gender_encoded
# 0  John  0.0   male               0
# 2  Jack  1.0   male               0

⚙️ Standalone Function Usage

Use this approach when you need fine-grained control or quick one-off transformations on specific parts of your data.

❓ Missing Handling Module

This module provides multiple strategies to handle missing data through these functions:

• handle_missing_values_drop: Drop-based strategy

  • Eliminate all rows that contain any NaN value.

• handle_missing_values_datatype_imputation: Data type–aware imputation

  • Fill missing numeric values using the specified strategy: mean, median, or mode.
  • Fill missing categorical values with the first mode of each column.

• handle_missing_values_adjacent_value_imputation: Value propagation or interpolation

  • Forward fill (ffill)
  • Backward fill (bfill)
  • Linear interpolation
  • Time-based interpolation (if datetime index is present)

import dataglass as dg
import pandas as pd
import numpy as np

# Creating a sample dataframe with a missing value
df = pd.DataFrame({
    "name": ["John", "Jane", "Jack"],
    "age": [40, np.nan, 50],
    "gender": ["male", "female", np.nan]
})

# Impute numeric columns using mean and the categorical columns using the first mode of that column
df_cleaned = dg.handle_missing_values_datatype_imputation(
    data = df,
    numeric_datatype_imputation_method = dg.NumericDatatypeImputationMethod.MEAN,
    verbose = True
)

print(f"Preprocessed Data:\n{df_cleaned}")

# =========== Expected Terminal Output =============

# Dataset has 3 rows before handling missing values.

# Missing values are:
# name      0
# age       1
# gender    1
# dtype: int64

# Dataset has 3 rows after handling missing values.

# Preprocessed Data:
#    name   age  gender
# 0  John  40.0    male
# 1  Jane  45.0  female
# 2  Jack  50.0  female

📑 Duplicate Handling Module

This module provides two strategies to handle duplicate data through these functions:

• handle_duplicate_values_exact: Remove exact duplicate rows

  • Optionally, a specific set of columns can be provided for duplicate analysis via columns_subset

• handle_duplicate_values_fuzzy: Remove approximate (fuzzy) duplicates based on string similarity

  • Define the similarity threshold (e.g., 70–90%)
  • Limit the comparison to specific columns via columns_subset

import dataglass as dg
import pandas as pd
import numpy as np

# Creating a sample dataframe with a similar name values
df = pd.DataFrame({
    "name": ["John", "Johney", "Jack"],
    "age": [40, 45, 50],
})

# Only "name" column will be used to detect fuzzy duplicates
columns_subset = ["name"]

# Remove rows that are 70% or more similar in the "name" column (It keeps the first occurrence of each similarity group)
df_cleaned = dg.handle_duplicate_values_fuzzy(
    data = df, 
    columns_subset = columns_subset, 
    similarity_thresholds = (70,100), 
    verbose = True)

print(f"Preprocessed Data:\n{df_cleaned}")

# =========== Expected Terminal Output =============

# Dataset has 3 rows before handling duplicate values.

# Top 10 of duplicate values are (Totally 2 rows - including all duplicates, but from each group first one will remain and others will be removed):
#      name  age
# 0    John   40
# 1  Johney   45

# Dataset has 2 rows after handling duplicate values.

# Preprocessed Data:
#    name  age
# 0  John   40
# 2  Jack   50

❗ Outlier Handling Module

This module separates the detection and handling of outliers, giving you flexibility and control.

• detect_outliers: Detects outliers using various statistical or model-based techniques:

  • IQR, ZSCORE, ISOLATION_FOREST, LOCAL_OUTLIER_FACTOR
  • An optional list of columns can be specified; otherwise, all numeric columns are used
  • Customization options like contamination_rate and n_neighbors available for model-based methods

• handle_outliers: Applies the selected strategy to the detected outliers

  • DROP: Remove rows containing outliers
  • REPLACE_WITH_MEDIAN: Replace outlier values with their column median
  • CAP_WITH_BOUNDARIES: Clip outlier values to the inlier boundary limits (based on the detection method)

import dataglass as dg
import pandas as pd
import numpy as np

# Sample dataset with an outlier in the "age" column
df = pd.DataFrame({
    "name": ["John", "Johney", "Jack", "Sara", "Chris"],
    "age": [40, 45, 30, 25, 200],
})

# Step 1: Detect outliers using the IQR method
outliers, boundaries = dg.detect_outliers(
    data = df, 
    detect_outlier_method = dg.DetectOutlierMethod.IQR)

print(f"Boundries:\n{boundaries}")

# Step 2: Cap outlier values with the calculated boundaries
df_cleaned = dg.handle_outliers(
    data = df,
    handle_outlier_method = dg.HandleOutlierMethod.CAP_WITH_BOUNDARIES,
    outliers = outliers,
    boundaries=boundaries,
    verbose=True)

# Visualize the outliers using boxplot and histograms before and after cleaning
dg.visualize_outliers(df, df_cleaned, "", dg.DetectOutlierMethod.IQR, dg.HandleOutlierMethod.CAP_WITH_BOUNDARIES)

print(f"Preprocessed Data:\n{df_cleaned}")

# =========== Expected Terminal Output =============

# Boundries:
# {'age': (np.float64(7.5), np.float64(67.5))}

# Dataset has 5 rows before handling outliers values.

# Top 10 of rows containing outliers are (Totally 1 rows):
#     name  age
# 4  Chris  200

# Dataset has 5 rows after handling outliers.

# Preprocessed Data:
#      name   age
# 0    John  40.0
# 1  Johney  45.0
# 2    Jack  30.0
# 3    Sara  25.0
# 4   Chris  67.5

# Visualizations have been saved in the 'visualizations' folder inside the project root directory.

🔢 Feature Encoding Module

This module provides multiple methods to encode categorical features into numerical representations suitable for machine learning.

• encode_feature:
  • Supported methods: LABEL_ENCODING, ONEHOT_ENCODING, HASHING
  • Optionally specify columns; otherwise, all categorical columns will be encoded
  • To apply different methods to different columns, call the function multiple times with desired parameters

import dataglass as dg
import pandas as pd
import numpy as np

# Sample dataset with a categorical "gender" column
df = pd.DataFrame({
    "name": ["John", "Jane", "Jack"],
    "age": [40, 45, 50],
    "gender": ["male", "female", "male"]
})

# Only "gender" column will be encoded
columns_subset = ["gender"]

# Convert "gender" to numerical labels (e.g., male=1, female=0)
df_cleaned = dg.encode_feature(
    data = df,
    feature_encoding_method = dg.FeatureEncodingMethod.LABEL_ENCODING,
    columns_subset = columns_subset)

print(f"Preprocessed Data:\n{df_cleaned}")

# =========== Expected Terminal Output =============

# Preprocessed Data:
#    name  age  gender  gender_encoded
# 0  John   40    male               1
# 1  Jane   45  female               0
# 2  Jack   50    male               1

🔁 Type Conversion Module

This module provides methods for converting column datatypes for better compatibility and precision.

• convert_datatype_auto:
  • Automatically infers and converts column datatypes based on heuristics.
• convert_datatype_userdefined:
  • Converts column datatypes based on a user-defined mapping scenario (supports formats like datetime parsing).

import dataglass as dg
import pandas as pd
import numpy as np

# Sample dataset with mixed types
df = pd.DataFrame({
    "name": ["John", "Jane", "Jack"],
    "age": [40.0, 45, 50.0],
    "signup_date": ["2023-01-01", "2023-01-01", "2023-03-01"]
})

# user-defined scenario to request how to convert specific columns
convert_scenario =  {
    "column": ["age", "signup_date"],
    "datatype": ["int", "datetime"],
    "format": ["", "%Y-%m-%d"]
}

# Apply type conversion using the user-defined configuration
df_cleaned = dg.convert_datatype_userdefined(
    data = df,
    convert_scenario = convert_scenario,
    verbose=True)

print(f"Preprocessed Data:\n{df_cleaned}")

# =========== Expected Terminal Output =============

# Before automatic datatype conversion, the datatype are as follows:
# name            object
# age            float64
# signup_date     object
# dtype: object

# After automatic datatype conversion, the datatype are as follows:
# name                   object
# age                     int64
# signup_date    datetime64[ns]
# dtype: object

# Preprocessed Data:
#    name  age signup_date
# 0  John   40  2023-01-01
# 1  Jane   45  2023-01-01
# 2  Jack   50  2023-03-01

📏 Feature Scaling Module

This module allows feature scaling using different methods on selected columns, with an optional L2 normalization across all numeric columns.

• scale_feature:
  • Supported scaling methods: MINMAX_SCALING, ZSCORE_STANDARDIZATION, ROBUST_SCALING
  • L2 normalization can be optionally applied to all numeric columns after scaling
  • Scaling can be customized per column using the scaling_scenario

import dataglass as dg
import pandas as pd
import numpy as np

# Sample dataset with numeric features
df = pd.DataFrame({
    "name": ["John", "Jane", "Jack"],
    "age": [40, 45, 50],
    "score": [60, 70, 180],
    "income": [5000, 4500, 3000]
})

# Define a scenario to scale "age" using MinMax and "score" using RobustScaler
scaling_scenario = {
    "column": ["age", "score", "income"],
    "scaling_method": ["MINMAX_SCALING", "ROBUST_SCALING", "ZSCORE_STANDARDIZATION"]
}

# Apply scaling and then L2 normalize all numeric features
df_cleaned = dg.scale_feature(
    data = df,
    scaling_scenario = scaling_scenario,
    apply_l2normalization = True)

print(f"Preprocessed Data:\n{df_cleaned}")

# =========== Expected Terminal Output =============

# Preprocessed Data:
#    name       age     score    income
# 0  John  0.000000 -0.167564  0.985861
# 1  Jane  0.786796  0.000000  0.617213
# 2  Jack  0.400137  0.733584 -0.549313

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✅ Requirements

• Python ≥ 3.10
  All other dependencies will be installed automatically via pip install dataglass.

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🛣️ Roadmap

• ✅ Preprocessing Modules
• ✅ Custom Pipelines
• ✅ Automatic Preprocessing
• ⏳ Exploratory Data Analysis (EDA)
• ⏳ Machine Learning Modules

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📄 License

This project is licensed under the BSD 3-Clause License.
See the LICENSE file in the repository for full details.

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🤝 Contributing

Contributions, bug reports, and feature requests are welcome!
Please open an issue or submit a pull request via GitHub.

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👤 Author

Saman Teymouri
Data Scientist/Analyst & Python Developer
Berlin, Germany