dataroom 1.0.1

A powerful and easy-to-use data processing library

# DataRoom

DataRoom is a powerful and easy-to-use Python library for data processing, cleaning, analysis, machine learning, and optimization. It provides an intuitive API with well-structured classes and functions, making it simple to work with data in various formats, including CSV, JSON, Excel, databases, and APIs.

## Features

- **Data Ingestion**: Load data from multiple sources, including CSV, JSON, Excel, databases, and APIs.
- **Data Cleaning**: Handle missing values, normalize data, encode categorical variables, and detect outliers.
- **Data Exploration**: Generate descriptive statistics, correlation matrices, and interactive plots.
- **Data Pipelines**: Automate data transformation and preprocessing.
- **Machine Learning Integration**: Train and evaluate classification and regression models.
- **Optimization**: Parallel processing and memory optimization for large datasets.

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## Installation

You can install DataRoom using pip:

```bash
pip install dataroom

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Usage

1. Data Ingestion

Load Data from Different Sources

from dataroom import DataIngestor

ingestor = DataIngestor()
df_csv = ingestor.from_csv("data.csv")
df_json = ingestor.from_json("data.json")
df_excel = ingestor.from_excel("data.xlsx")
df_sql = ingestor.from_sql("sqlite:///database.db", "SELECT * FROM users")
df_api = ingestor.from_api("https://api.example.com/data")

print(df_csv.head())

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2. Data Cleaning

Handle Missing Values

from dataroom import DataCleaner

cleaner = DataCleaner()
df_clean = cleaner.handle_missing(df_csv, strategy="mean")  # Fill missing values with column mean
print(df_clean.head())

Encode Categorical Data

df_encoded = cleaner.encode(df_clean, encoding_type="onehot")
print(df_encoded.head())

Detect and Remove Outliers

df_no_outliers = cleaner.detect_outliers(df_encoded, method="iqr")
print(df_no_outliers.head())

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3. Data Exploration

Generate Summary Statistics

from dataroom import DataExplorer

explorer = DataExplorer()
print(explorer.describe(df_no_outliers))

Plot Data

explorer.plot(df_no_outliers, kind="hist")  # Histogram

Generate a Data Profile Report

profile_report = explorer.profile(df_no_outliers)
print(profile_report)

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4. Data Pipeline

Automate Data Processing

from dataroom import DataPipeline

pipeline = DataPipeline()
pipeline.add_step(cleaner.normalize, method="minmax")
pipeline.add_step(lambda data: cleaner.handle_missing(data, strategy="median"))

df_pipeline = pipeline.run(df_no_outliers)
print(df_pipeline.head())

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5. Machine Learning Integration

Train a Classification Model

from dataroom import DataML

ml_module = DataML()
df_pipeline["target"] = [0, 1, 0, 1, 0, 1]

model, score = ml_module.train_model(df_pipeline, target="target", model_type="classification")
print("Model Accuracy:", score)

Make Predictions

predictions = ml_module.predict(model, df_pipeline.drop(columns=["target"]))
print(predictions)

Auto Feature Selection

selected_features = ml_module.auto_feature_selection(df_pipeline, target="target", method="correlation")
print("Selected Features:", selected_features)

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6. Optimization

Parallel Processing

from dataroom import DataOptimizer

optimizer = DataOptimizer()

def process_function(x):
    return x * 2

parallel_result = optimizer.parallel_process(process_function, [1, 2, 3, 4, 5])
print(parallel_result)

Optimize Memory Usage

df_optimized = optimizer.optimize_memory(df_pipeline)
print(df_optimized.info())

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Coded By Mohammad Taha Gorji

License

This project is licensed under the MIT License.