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AutoViz: The One-Line Automatic Data Visualization Library


Unlock the power of AutoViz to visualize any dataset, any size, with just a single line of code! Plus, now you can get a quick assessment of your dataset's quality and fix DQ issues through the FixDQ() function.

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With AutoViz, you can easily and quickly generate insightful visualizations for your data. Whether you're a beginner or an expert in data analysis, AutoViz can help you explore your data and uncover valuable insights. Try it out and see the power of automated visualization for yourself!

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The latest updates about autoviz library can be found in Updates page.


Starting with version 0.1.801, an important Update Regarding Dependency Management

We're excited to announce a significant update to AutoViz that enhances compatibility with various Python versions and streamlines dependency management!

  • Modular Dependency Loading: AutoViz now uses a more flexible approach for importing visualization libraries starting with version `0.1.801`. This means you only need to install certain dependencies (like hvplot and holoviews) if you plan to use specific backends (e.g., bokeh). This change significantly reduces installation issues for users on newer Python versions such as 3.10 and higher.
  • Improved Backend Support: Depending on your Python environment, AutoViz dynamically adjusts to use compatible visualization libraries, ensuring a smoother user experience. Requirements: "holoviews>=1.14.9", "bokeh>=2.4.2", "hvplot>=0.7.3", "panel>=0.12.6".
  • What Does This Mean for You?

  • Easier Installation: If you've faced challenges installing AutoViz due to dependency conflicts, this update is for you. Now, you can install AutoViz without needing to install all its visualization dependencies upfront.
  • Tailored Usage: Choose the visualization backend that works best for your environment. AutoViz will handle the rest, importing necessary libraries as needed.
  • Seamless Compatibility: Users on the latest Python versions (like 3.11 and 3.12) can now enjoy a hassle-free AutoViz experience.
  • How to Update?

    Simply pull the latest version of AutoViz (0.1.801 and higher) from the pip repository. The modular dependency system will be automatically applied.


    Your feedback is crucial! If you encounter any issues or have suggestions, please let us know through GitHub Issues

    Thank you for your continued support and happy visualizing!


    If you use AutoViz in your research project or paper, please use the following format for citations:

    "Seshadri, Ram (2020). GitHub - AutoViML/AutoViz: Automatically Visualize any dataset, any size with a single line of code. source code:"

    Current citations for AutoViz

    Google Scholar


    The motivation behind the creation of AutoViz is to provide a more efficient, user-friendly, and automated approach to exploratory data analysis (EDA) through quick and easy data visualization plus data quality. The library is designed to help users understand patterns, trends, and relationships in the data by creating insightful visualizations with minimal effort. AutoViz is particularly useful for beginners in data analysis as it abstracts away the complexities of various plotting libraries and techniques. For experts, it provides another expert tool that they can use to provide inights into data that they may have missed.

    AutoViz is a powerful tool for generating insightful visualizations with minimal effort. Here are some of its key selling points compared to other automated EDA tools:

    1. Ease of use: AutoViz is designed to be user-friendly and accessible to beginners in data analysis, abstracting away the complexities of various plotting libraries
    2. Speed: AutoViz is optimized for speed and can generate multiple insightful plots with just a single line of code
    3. Scalability: AutoViz is designed to work with datasets of any size and can handle large datasets efficiently
    4. Automation: AutoViz automates the visualization process, requiring just a single line of code to generate multiple insightful plots
    5. Customization: AutoViz provides several options for customizing the visualizations, such as changing the chart type, color palette, etc.
    6. Data Quality: AutoViz now provides data quality assessment by default and helps you fix DQ issues with a single line of code using the FixDQ() function
    ## Installation


    Create a new environment and install the required dependencies to clone AutoViz:

    From PyPi:

    cd <AutoViz_Destination>
    git clone
    # or download and unzip
    conda create -n <your_env_name> python=3.7 anaconda
    conda activate <your_env_name> # ON WINDOWS: `source activate <your_env_name>`
    cd AutoViz
    pip install -r requirements.txt


    Discover how to use AutoViz in this Medium article.

    In the AutoViz directory, open a Jupyter Notebook or open a command palette (terminal) and use the following code to instantiate the AutoViz_Class. You can simply run this code step by step:

    from autoviz import AutoViz_Class
    AV = AutoViz_Class()
    dft = AV.AutoViz(filename)

    AutoViz can use any input either filename (in CSV, txt, or JSON format) or a pandas dataframe. If you have a large dataset, you can set the max_rows_analyzed and max_cols_analyzed arguments to speed up the visualization by asking autoviz to sample your dataset.

    AutoViz can also create charts in multiple formats using the chart_format setting:

    • If chart_format ='png' or 'svg' or 'jpg': Matplotlib charts are plotted inline.
      • Can be saved locally (using verbose=2 setting) or displayed (verbose=1) in Jupyter Notebooks.
      • This is the default behavior for AutoViz.
    • If chart_format='bokeh': Interactive Bokeh charts are plotted in Jupyter Notebooks.
    • If chart_format='server', dashboards will pop up for each kind of chart on your browser.
    • If chart_format='html', interactive Bokeh charts will be created and silently saved as HTML files under the AutoViz_Plots directory (under working folder) or any other directory that you specify using the save_plot_dir setting (during input).


    Arguments for AV.AutoViz() method:

    • filename: Use an empty string ("") if there's no associated filename and you want to use a dataframe. In that case, using the dfte argument for the dataframe. Otherwise provide a filename and leave dfte argument with an empty string. Only one of them can be used.
    • sep: File separator (comma, semi-colon, tab, or any column-separating value) if you use a filename above.
    • depVar: Target variable in your dataset; set it as an empty string if not applicable.
    • dfte: name of the pandas dataframe for plotting charts; leave it as empty string if using a filename.
    • header: set the row number of the header row in your file (0 for the first row). Otherwise leave it as 0.
    • verbose: 0 for minimal info and charts, 1 for more info and charts, or 2 for saving charts locally without display.
    • lowess: Use regression lines for each pair of continuous variables against the target variable in small datasets; avoid using for large datasets (>100,000 rows).
    • chart_format: 'svg', 'png', 'jpg', 'bokeh', 'server', or 'html' for displaying or saving charts in various formats, depending on the verbose option.
    • max_rows_analyzed: Limit the max number of rows to use for visualization when dealing with very large datasets (millions of rows). A statistically valid sample will be used by autoviz. Default is 150000 rows.
    • max_cols_analyzed: Limit the number of continuous variables to be analyzed. Defaul is 30 columns.
    • save_plot_dir: Directory for saving plots. Default is None, which saves plots under the current directory in a subfolder named AutoViz_Plots. If the save_plot_dir doesn't exist, it will be created.


    Here are some examples to help you get started with AutoViz. If you need full jupyter notebooks with code samples they can be found in examples folder.

    Example 1: Visualize a CSV file with a target variable

    from autoviz import AutoViz_Class
    AV = AutoViz_Class()
    filename = "your_file.csv"
    target_variable = "your_target_variable"
    dft = AV.AutoViz(


    Example 2: Visualize a Pandas DataFrame without a target variable:

    import pandas as pd
    from autoviz import AutoViz_Class
    AV = AutoViz_Class()
    data = {'col1': [1, 2, 3, 4, 5], 'col2': [5, 4, 3, 2, 1]}
    df = pd.DataFrame(data)
    dft = AV.AutoViz(


    Example 3: Generate interactive Bokeh charts and save them as HTML files in a custom directory

    from autoviz import AutoViz_Class
    AV = AutoViz_Class()
    filename = "your_file.csv"
    target_variable = "your_target_variable"
    custom_plot_dir = "your_custom_plot_directory"
    dft = AV.AutoViz(


    These examples should give you an idea of how to use AutoViz with different scenarios and settings. By tailoring the options and settings, you can generate visualizations that best suit your needs, whether you're working with large datasets, interactive charts, or simply exploring the relationships between variables.


    AutoViz is actively maintained and improved by a team of dedicated developers. If you have any questions, suggestions, or issues, feel free to reach out to the maintainers:


    We welcome contributions from the community! If you're interested in contributing to AutoViz, please follow these steps:

    • Fork the repository on GitHub.
    • Clone your fork and create a new branch for your feature or bugfix.
    • Commit your changes to the new branch, ensuring that you follow coding standards and write appropriate tests.
    • Push your changes to your fork on GitHub.
    • Submit a pull request to the main repository, detailing your changes and referencing any related issues.

    See the contributing file!


    AutoViz is released under the Apache License, Version 2.0. By using AutoViz, you agree to the terms and conditions specified in the license.


    Here are some additional tips and reminders to help you make the most of the library:

    • Make sure to regularly upgrade AutoViz to benefit from the latest features, bug fixes, and improvements. You can update it using pip install --upgrade autoviz.
    • AutoViz is highly customizable, so don't hesitate to explore and experiment with various settings, such as chart_format, verbose, and max_rows_analyzed. This will allow you to create visualizations that better suit your specific needs and preferences.
    • Remember to delete the AutoViz_Plots directory (or any custom directory you specified) periodically if you used the verbose=2 option, as it can accumulate a large number of saved charts over time.
    • For further guidance or inspiration, check out the Medium article on AutoViz, as well as other online resources and tutorials.
    • AutoViz will visualize any sized file using a statistically valid sample.
    • COMMA is the default separator in the file, but you can change it.
    • Assumes the first row as the header in the file, but this can be changed.
    • By leveraging AutoViz's powerful and flexible features, you can streamline your data visualization process and gain valuable insights more efficiently. Happy visualizing!


    This project is not an official Google project. It is not supported by Google, and Google specifically disclaims all warranties as to its quality, merchantability, or fitness for a particular purpose.


    Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.








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