Data Analysis Toolkit

Description:

DataAnalysisToolkit is a comprehensive Python package offering a suite of tools designed for efficient data analysis. It simplifies common data analysis tasks, such as loading CSV data, performing statistical analysis, cleaning datasets, visualizing results, and preparing data for machine learning workflows. This toolkit is an invaluable resource for data analysts, data scientists, and anyone involved in data exploration or machine learning.

Features:

Core Functionalities:

• Data Loading: Load data directly from CSV files into a Python environment for analysis.
• Statistical Analysis: Compute essential statistics like mean, median, mode, and trimmed mean.
• Outlier Detection: Identify anomalies using the z-score method.
• Data Cleaning: Handle missing values, drop duplicates, and encode categorical data for seamless integration into workflows.
• Data Splitting: Split datasets into training and testing sets, ready for machine learning models.
• Data Visualization: Generate insightful visualizations such as histograms for data exploration.
• Data Export: Save cleaned and processed data back to CSV format.

Enhanced Functionalities:

• Advanced Visualization: Generate a variety of informative plots using a dedicated visualization tool.
• Feature Engineering: Create and enhance dataset features for better analysis and modeling.
• Model Evaluation: Assess the performance of machine learning models using in-built evaluation tools.
• Report Generation: Automatically generate HTML reports summarizing the analysis and visualizations.
• Data Imputation: Use advanced techniques to fill missing values effectively.

Requirements:

To use DataAnalysisToolkit, ensure your environment meets the following requirements:

• Python 3.8 or higher
• Pandas >= 1.3.0
• Numpy >= 1.21.0
• Matplotlib >= 3.4.0
• Scikit-learn >= 1.0.0

Install these dependencies using the following command:

bash

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pip install pandas numpy matplotlib scikit-learn

Instructions:

Installation

Install DataAnalysisToolkit via pip:

bash

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pip install dataanalysistoolkit

Getting Started

Here’s a quick example to get you started with DataAnalysisToolkit:

python

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from data_analysis_toolkit import DataAnalysisToolkit # Initialize the toolkit with the path to a CSV file analyzer = DataAnalysisToolkit('../data/test.csv') # Example 1: Perform statistical analysis on a column statistics = analyzer.calculate_budget_statistics('column_name') print(statistics) # Example 2: Detect outliers in a column outliers = analyzer.detect_outliers('column_name') print(outliers) # Example 3: Handle missing values in a column analyzer.handle_missing_values('column_name', strategy='fill', fill_value=0) # Example 4: Drop duplicate rows analyzer.drop_duplicates() # Example 5: Encode categorical features analyzer.encode_categorical_features() # Example 6: Split data for machine learning X_train, X_test, y_train, y_test = analyzer.split_data('target_column') # Example 7: Visualize data with a histogram analyzer.plot_data('column_name') # Example 8: Export the cleaned data to a new CSV file analyzer.export_data('new_file.csv')