🧹 Cleanly: CSV Cleaner and EDA Tool

Cleanly is an interactive Streamlit-based tool that allows you to upload, clean, and analyze CSV files easily. It provides functionalities for data cleaning, outlier removal, data visualization, and exploratory data analysis (EDA).

👉 Try it now: Launch Cleanly App - No installation required!

✨ Features

• 📂 CSV File Upload: Upload CSV files for processing.
• 🧹 Data Cleaning:
  • 🗑️ Remove duplicate rows.
  • 🩹 Handle missing values (fill with mean or mode based on data type).
• 🚫 Outlier Removal:
  • 📊 Based on Z-Score.
  • 📈 Based on Interquartile Range (IQR).
• 📏 Data Normalization: Scale numeric values between 0 and 1.
• 🔢 Categorical Column Encoding: Convert categorical columns into numeric values.
• ✏️ Rename and Delete Columns: Modify or delete specific columns.
• 🔍 Row Filtering and Sorting: Filter rows based on specific values and sort data.
• 📊 Data Visualization:
  • 📉 Histograms.
  • 📊 Bar charts.
  • 🟢 Scatter plots.
  • 🔗 Correlation matrix with automated insights.
• 📚 Data Grouping: Group data by columns and apply aggregation functions like mean, sum, count, etc.
• 🔎 Enhanced Data Exploration:
  • 📋 Data type analysis with first value examples
  • 📊 Distribution of column data types visualization
  • 🔍 Automatic outlier detection with statistical measurements
  • 📈 Top outlier columns visualization
• 💾 Download Processed Data: Download the cleaned and processed CSV file.

🛠️ Requirements

Before running the project, ensure you have the following dependencies installed:

• 🐍 Python 3.7 or higher
• 📦 Streamlit
• 📦 Pandas
• 📦 NumPy
• 📦 Seaborn
• 📦 Matplotlib

📝 Usage

📂 Upload a CSV File:

Click the "Browse files" button to upload a CSV file from your computer.

🔍 Initial Exploration:

View the original data, the number of rows and columns, duplicates, and missing values.

⚙️ Select Actions:

Use the dropdown menu to select actions such as cleaning data, removing outliers, normalizing data, etc.

📊 Data Visualization:

Generate visualizations such as histograms, bar charts, scatter plots, and the correlation matrix to analyze the data.

💾 Download Processed Data:

Once you have finished processing the data, click the download button to save the cleaned CSV file.

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🔍 Detailed Functionalities

📊 Data Analysis

• Descriptive Statistics: Generate summary statistics (count, mean, std, min, max, quartiles) for numeric columns.
• Correlation Matrix: Displays the correlation between numeric columns as a heatmap with automated insights.

🧹 Data Cleaning

• Remove Duplicates: Identifies and removes duplicate rows in the dataset.
• Handle Missing Values:
  • For numeric columns, missing values are filled with the mean.
  • For categorical columns, missing values are filled with the mode.

🚫 Outlier Removal

• Z-Score: Removes rows where numeric values have a Z-Score greater than a threshold (default: 3).
• IQR (Interquartile Range): Removes rows where numeric values fall outside the IQR range.

🔄 Data Transformation

• Normalize Data: Scale numeric columns to a range between 0 and 1 for better comparability.
• Encode Categorical Columns: Convert text/categorical columns to numeric values for analysis.
• Rename Columns: Change column names for better clarity or consistency.
• Delete Specific Columns: Remove unnecessary columns from the dataset.

🔍 Data Filtering & Sorting

• Filter Rows: Filter data based on specific column values with options for different comparison types.
• Sort Data: Order the dataset by any column in ascending or descending order.

📊 Data Visualization

• Histograms: Displays the distribution of numeric values.
• Bar Charts: Shows the frequency of categorical values.
• Scatter Plots: Visualizes the relationship between two numeric columns.
• Correlation Matrix: Displays the correlation between numeric columns as a heatmap.

📚 Data Grouping and Aggregation

• Group data by a selected column and apply aggregation functions such as mean, sum, count, max, or min.

💾 Download Processed Data

• Download the processed CSV file with all the applied changes.

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🛠️ Example Workflow

1. 📂 Upload a raw CSV file.
2. 🧹 Select "Clean Data" to remove duplicates and handle missing values.
3. 🚫 Use "Remove Outliers (Z-Score)" to eliminate outliers.
4. 📊 Generate a histogram to analyze the distribution of a numeric column.
5. 💾 Download the cleaned file by clicking "Download Cleaned CSV."

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📸 Screenshots

Below is a preview of Cleanly's interface. The application provides an intuitive dashboard where you can upload your CSV files, clean your data, and perform exploratory data analysis with just a few clicks.

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🤝 Contributions

If you want to contribute to this project, please follow these steps:

1. 🍴 Fork the repository.
2. 🌿 Create a new branch for your feature (git checkout -b feature/new-feature).
3. 💻 Make your changes and commit them (git commit -m "Added new feature").
4. 📤 Push your branch (git push origin feature/new-feature).
5. 🔁 Open a Pull Request.

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📜 License

This project is licensed under the MIT License. See the LICENSE file for more details.