| Name | df2tables JSON |
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| Summary | df2tables: Pandas and Polars DataFrames to Interactive DataTables |
| upload_time | 2025-10-11 09:35:52 |
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| requires_python | None |
| license | MIT License
Copyright (c) 2024 Tomasz Sługocki
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| keywords |
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html
javascript
pandas
polars
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# df2tables - Pandas & Polars DataFrames to Interactive HTML Tables
[](https://pypi.org/project/df2tables/)
`df2tables` is a Python utility for exporting **Pandas** and **Polars** DataFrames into interactive HTML tables using [DataTables](https://datatables.net/) - a powerful JavaScript library.
It’s built to embed seamlessly into Flask, Django, FastAPI, or any web framework.
By rendering tables directly from JavaScript arrays, this tool delivers **fast performance and compact file sizes**, enabling smooth browsing of large datasets while maintaining full responsiveness.
**Minimal dependencies**: only `pandas` ***or*** `polars` (you don’t need pandas installed if using polars).
Converting a DataFrame to an interactive table takes just one function call:
```python
render(df, **kwargs)
# or for web frameworks:
render_inline(df, **kwargs)
```
## Features
- Converts `pandas` and `polars` dataframes interactive standalone HTML tables
- **Web Framework Ready**: Specifically designed for easy embedding in Flask, Django, FastAPI, and other web frameworks
- Browse **large datasets** using filters and sorting
- Works **independently of Jupyter** or live server (though [notebook](#rendering-in-notebook) rendering is supported)
- Useful for training dataset inspection and feature engineering: Quickly browse through large datasets, identify outliers, and data quality issues interactively
- **Customization**: [Configuring DataTables from Python](#customization-configuring-datatables-from-python) *(new)*
## Screenshots
A standalone HTML file containing a JavaScript array as data source for DataTables has several advantages. For example, you can browse quite large datasets locally.
The column control feature provides dropdown filters for categorical data and search functionality for text columns, enhancing data exploration capabilities through the excellent [DataTables Column Control extension](https://datatables.net/extensions/columncontrol/).
*Note: By default, filtering is enabled for all non-numeric columns.*
## Quick Start
The simplest function call with default arguments is:
```python
df2tables.render(df, to_file='df.html')
```
## Installation
```bash
pip install df2tables
```
### Sample DataFrame
```python
#render sample DataFrame
html_string = df2t.render_sample_df(to_file="sample_table.html")
```
### Rendering in notebook
```python
from df2tables import render_nb
render_nb(df) #show interactive table in jupyter notebook
```
*Note: Notebook rendering is currently supported in Jupyter, VS Code notebooks and marimo*
## Main Functions
### render
```python
df2t.render(
df: pd.DataFrame,
to_file: Optional[str] = None,
title: str = "Title",
precision: int = 2,
num_html: List[str] = None,
startfile: bool = True,
templ_path: str = TEMPLATE_PATH,
load_column_control: bool = True,
js_opts: Optional[dict] = None,
display_logo: bool = True
) -> Union[str, file_object]
```
**Parameters:**
- `df`: Input pandas DataFrame
- `to_file`: Output HTML file path. If None, returns HTML string instead of writing file
- `title`: Title for the HTML table
- `precision`: Number of decimal places for floating-point numbers
- `num_html`: List of numeric column names to render with color-coded HTML formatting (negative values in red)
- `startfile`: If True, automatically opens the generated HTML file in default browser
- `templ_path`: Path to custom HTML template (uses default if not specified)
- `load_column_control`: If True, integrates the [DataTables Column Control extension](https://datatables.net/extensions/columncontrol/) programmatically for enhanced filtering and search capabilities (default: True)
- `js_opts`: Dictionary of [DataTables configuration options](https://datatables.net/reference/option/) to customize table behavior (e.g., pagination, scrolling, layout, language).(default: None)
- `display_logo`: If True, displays DataTables logo (default: True)
**Returns:**
- HTML string if `to_file=None`
- File object if `to_file` is specified
### render_inline
```python
df2t.render_inline(
df: pd.DataFrame,
table_attrs: Dict = None,
**kwargs
) -> str
```
**Parameters:**
This function is designed for integration with web applications and has the following characteristics:
- Returns only the `<table>` markup and the associated JavaScript
- Excludes `<html>`, `<head>`, and `<body>` tags
- Useful for pages with multiple tables, as you can assign unique IDs via the `table_attrs` dictionary (e.g., `{'id': 'my-unique-table'}`)
- **Important**: This function does not include jQuery or DataTables library dependencies. You must include them manually in your host HTML page for the table to function correctly
Some key arguments are not applicable, such as `title` or `display_logo`, because the returned HTML contains only the table and JavaScript.
The **new** additional **`table_attrs`** argument accepts a dictionary of HTML table attributes, such as an ID or CSS class. This is especially useful for multiple tables on a single page (each must have a different ID).
See an example of multiple tables with different configuration options placed in separate tabs (jQuery UI Tabs): [flask_multiple_tables_tabs.py](https://github.com/ts-kontakt/df2tables/blob/main/flask_multiple_tables_tabs.py)
### Minimal Flask Example
Here's a complete, minimal **working** Flask application that demonstrates how to properly embed a DataTable with all required dependencies:
```python
import df2tables as df2t
from flask import Flask, render_template_string
app = Flask(__name__)
@app.route("/")
def home():
# Generate sample data (or use your own DataFrame)
df = df2t.get_sample_df()
df_title = "DataFrame Rendered as DataTable inline in <strong>Flask</strong>"
string_datatable = df2t.render_inline(df)
# Embed in a complete HTML template with all required dependencies
return render_template_string(
"""
<!DOCTYPE html>
<html>
<head>
<title>Flask Data Dashboard</title>
<meta name="viewport" content="width=device-width, initial-scale=1">
<script src="https://code.jquery.com/jquery-3.7.1.min.js"></script>
<link href="https://cdn.datatables.net/2.3.4/css/dataTables.dataTables.min.css" rel="stylesheet">
<script src="https://cdn.datatables.net/2.3.4/js/dataTables.min.js"></script><!--[column_control-->
<link href="https://cdn.datatables.net/columncontrol/1.1.0/css/columnControl.dataTables.min.css" rel="stylesheet">
<script src="https://cdn.datatables.net/columncontrol/1.1.0/js/dataTables.columnControl.min.js"></script><!--column_control]-->
<!-- Optional: PureCSS for styling -->
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/purecss@3.0.0/build/pure-min.css">
</head>
<body style="background-color: #f4f4f4;">
<div style="background-color: #fff; padding: 20px; margin: 20px;">
<h1>My Flask Data Dashboard</h1>
{{ inline_datatable | safe }}
</div>
</body>
</html>
""",
inline_datatable=string_datatable,
)
if __name__ == "__main__":
app.run(debug=True)
```
*Note: Pandas DataFrame indexes are not rendered by default. If you want to enable indexes in an HTML table, simply call `df2tables.render(df.reset_index(), args...)`*
## Customization: Configuring DataTables from Python
You can now customize DataTables behavior directly from Python by passing configuration options through the new `js_opts` parameter. This allows you to control [DataTables options](https://datatables.net/reference/option/) and [features](https://datatables.net/reference/feature/) without modifying the HTML template.
### Basic Usage
Simply pass a dictionary of DataTables options to the `js_opts` parameter in the `render` or `render_inline` function.
### Examples
#### 1. Customize Layout and Language
Rearrange control elements (such as the search bar and info display) using the `layout` option, or localize text:
```python
custom_cfg = {
"language": {
"searchPlaceholder": "Search in all text columns"
},
"pageLength": 25, # number of table rows showing
"layout": {
"topStart": "info",
"top1Start": "pageLength",
"topEnd": "search",
"bottomEnd": "paging"
}
}
df2t.render(df, js_opts=custom_cfg, to_file="localized_table.html")
```
#### 2. Replace Paging with Scrolling
Disable pagination and enable vertical and horizontal scrolling for easier navigation of large datasets.
_Note_: Using `scrollY` with disabled `paging` can be slow for large DataFrames.
```python
scroll_cfg = {
"paging": False, # slow for large tables
"scrollCollapse": False,
"scrollY": '50vh',
"scrollX": "70vw"
}
df2t.render(df, js_opts=scroll_cfg, to_file="scrolling_table.html")
```
### Error Handling
Invalid keys are ignored by DataTables, so malformed or non-existent options **usually** will not break table rendering.
**Note**: While invalid keys should be ignored, using a valid key with an incorrect *value type* may still cause an error in the browser's JavaScript console.
### Available Configuration Options
**Important Notes**
Some configuration options require additional DataTables extensions or plugins (e.g., Buttons, FixedHeader). At this time, such plugin-dependent options are not yet supported.
For best results, start with core features such as `layout` or `language` options before exploring more advanced configurations.
For a complete list of available settings, refer to the official DataTables documentation and:
https://datatables.net/examples/
It’s best to start with the core DataTables Features before adding advanced configurations.
* [Feature Reference](https://datatables.net/reference/feature/)
* [Configuration Options Reference](https://datatables.net/reference/option/)
* [Layout Configuration](https://datatables.net/reference/option/layout)
* [Language configuration ](https://datatables.net/reference/option/language)
### Column Name Formatting
For better readability in table headers, `df2tables` automatically converts underscores to spaces in column names. This improves word wrapping and prevents excessively wide columns.
To disable this automatic word wrapping behavior, add the following CSS to your custom template:
```css
span.dt-column-title {
white-space: nowrap;
}
```
### Bulk Dataset Processing
For exploratory data analysis across multiple datasets, you can generate tables programmatically.
The example below uses the [vega_datasets](https://github.com/altair-viz/vega_datasets) package, which provides easy access to a variety of sample datasets commonly used in data visualization and analysis.
*Note: Install vega_datasets with `pip install vega_datasets` to run this example.*
[Quick Browse First 10 Vega Datasets](https://github.com/ts-kontakt/df2tables/blob/main/bulk_dataset_processing.py)
### Error Handling
The module includes error handling for:
- **JSON serialization**: Custom encoder handles complex pandas or Python data types
- **Column compatibility**: Automatically converts problematic column types to string representation
### Offline Usage
*Note: "Offline" viewing assumes internet connectivity for CDN resources (DataTables, jQuery, PureCSS, [DataTables Column Control extension](https://datatables.net/extensions/columncontrol/)). For truly offline usage, modify the template to reference local copies of these libraries instead of CDN links.*
## Appendix: Template Customization
Templates use [comnt](https://github.com/ts-kontakt/comnt), a minimal markup system based on HTML/JS comments.
### Custom Templates
Copy and modify `datatable_templ.html` to apply custom styling or libraries, then pass the new template path to `templ_path`.
### Handle Pandas MultiIndex Columns (Experimental)
MultiIndex columns are automatically flattened with underscore separation.
## License
MIT License
© Tomasz Sługocki
Raw data
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"description": "# df2tables - Pandas & Polars DataFrames to Interactive HTML Tables\n\n[](https://pypi.org/project/df2tables/)\n\n`df2tables` is a Python utility for exporting **Pandas** and **Polars** DataFrames into interactive HTML tables using [DataTables](https://datatables.net/) - a powerful JavaScript library. \nIt\u2019s built to embed seamlessly into Flask, Django, FastAPI, or any web framework.\n\nBy rendering tables directly from JavaScript arrays, this tool delivers **fast performance and compact file sizes**, enabling smooth browsing of large datasets while maintaining full responsiveness.\n\n**Minimal dependencies**: only `pandas` ***or*** `polars` (you don\u2019t need pandas installed if using polars).\n\nConverting a DataFrame to an interactive table takes just one function call:\n\n```python\nrender(df, **kwargs) \n# or for web frameworks:\nrender_inline(df, **kwargs)\n```\n\n## Features\n\n- Converts `pandas` and `polars` dataframes interactive standalone HTML tables\n- **Web Framework Ready**: Specifically designed for easy embedding in Flask, Django, FastAPI, and other web frameworks\n- Browse **large datasets** using filters and sorting \n- Works **independently of Jupyter** or live server (though [notebook](#rendering-in-notebook) rendering is supported) \n- Useful for training dataset inspection and feature engineering: Quickly browse through large datasets, identify outliers, and data quality issues interactively\n- **Customization**: [Configuring DataTables from Python](#customization-configuring-datatables-from-python) *(new)*\n\n## Screenshots\n\n\n\nA standalone HTML file containing a JavaScript array as data source for DataTables has several advantages. For example, you can browse quite large datasets locally.\n\nThe column control feature provides dropdown filters for categorical data and search functionality for text columns, enhancing data exploration capabilities through the excellent [DataTables Column Control extension](https://datatables.net/extensions/columncontrol/).\n\n*Note: By default, filtering is enabled for all non-numeric columns.*\n\n## Quick Start\n\nThe simplest function call with default arguments is:\n\n```python\ndf2tables.render(df, to_file='df.html')\n```\n\n## Installation\n\n```bash\npip install df2tables\n```\n\n### Sample DataFrame\n\n```python\n#render sample DataFrame\nhtml_string = df2t.render_sample_df(to_file=\"sample_table.html\")\n```\n### Rendering in notebook\n```python\nfrom df2tables import render_nb\n\nrender_nb(df) #show interactive table in jupyter notebook\n```\n*Note: Notebook rendering is currently supported in Jupyter, VS Code notebooks and marimo*\n\n## Main Functions\n### render\n```python\ndf2t.render(\n df: pd.DataFrame,\n to_file: Optional[str] = None,\n title: str = \"Title\",\n precision: int = 2,\n num_html: List[str] = None,\n startfile: bool = True,\n templ_path: str = TEMPLATE_PATH,\n load_column_control: bool = True,\n js_opts: Optional[dict] = None,\n display_logo: bool = True\n) -> Union[str, file_object]\n```\n**Parameters:**\n- `df`: Input pandas DataFrame\n- `to_file`: Output HTML file path. If None, returns HTML string instead of writing file\n- `title`: Title for the HTML table\n- `precision`: Number of decimal places for floating-point numbers\n- `num_html`: List of numeric column names to render with color-coded HTML formatting (negative values in red)\n- `startfile`: If True, automatically opens the generated HTML file in default browser\n- `templ_path`: Path to custom HTML template (uses default if not specified)\n- `load_column_control`: If True, integrates the [DataTables Column Control extension](https://datatables.net/extensions/columncontrol/) programmatically for enhanced filtering and search capabilities (default: True)\n- `js_opts`: Dictionary of [DataTables configuration options](https://datatables.net/reference/option/) to customize table behavior (e.g., pagination, scrolling, layout, language).(default: None)\n- `display_logo`: If True, displays DataTables logo (default: True)\n\n**Returns:**\n- HTML string if `to_file=None`\n- File object if `to_file` is specified\n\n### render_inline\n\n```python\ndf2t.render_inline(\n df: pd.DataFrame, \n table_attrs: Dict = None,\n **kwargs\n) -> str\n```\n\n**Parameters:**\n\nThis function is designed for integration with web applications and has the following characteristics:\n\n- Returns only the `<table>` markup and the associated JavaScript\n- Excludes `<html>`, `<head>`, and `<body>` tags\n- Useful for pages with multiple tables, as you can assign unique IDs via the `table_attrs` dictionary (e.g., `{'id': 'my-unique-table'}`)\n- **Important**: This function does not include jQuery or DataTables library dependencies. You must include them manually in your host HTML page for the table to function correctly\n\nSome key arguments are not applicable, such as `title` or `display_logo`, because the returned HTML contains only the table and JavaScript.\n\nThe **new** additional **`table_attrs`** argument accepts a dictionary of HTML table attributes, such as an ID or CSS class. This is especially useful for multiple tables on a single page (each must have a different ID).\n\nSee an example of multiple tables with different configuration options placed in separate tabs (jQuery UI Tabs): [flask_multiple_tables_tabs.py](https://github.com/ts-kontakt/df2tables/blob/main/flask_multiple_tables_tabs.py)\n\n\n### Minimal Flask Example\nHere's a complete, minimal **working** Flask application that demonstrates how to properly embed a DataTable with all required dependencies:\n\n```python\nimport df2tables as df2t\nfrom flask import Flask, render_template_string\n\napp = Flask(__name__)\n\n@app.route(\"/\")\ndef home():\n # Generate sample data (or use your own DataFrame)\n df = df2t.get_sample_df()\n df_title = \"DataFrame Rendered as DataTable inline in <strong>Flask</strong>\"\n\n string_datatable = df2t.render_inline(df)\n\n # Embed in a complete HTML template with all required dependencies\n return render_template_string(\n \"\"\"\n <!DOCTYPE html>\n <html>\n <head>\n <title>Flask Data Dashboard</title>\n <meta name=\"viewport\" content=\"width=device-width, initial-scale=1\">\n \n <script src=\"https://code.jquery.com/jquery-3.7.1.min.js\"></script>\n <link href=\"https://cdn.datatables.net/2.3.4/css/dataTables.dataTables.min.css\" rel=\"stylesheet\">\n <script src=\"https://cdn.datatables.net/2.3.4/js/dataTables.min.js\"></script><!--[column_control-->\n <link href=\"https://cdn.datatables.net/columncontrol/1.1.0/css/columnControl.dataTables.min.css\" rel=\"stylesheet\">\n <script src=\"https://cdn.datatables.net/columncontrol/1.1.0/js/dataTables.columnControl.min.js\"></script><!--column_control]-->\n\n <!-- Optional: PureCSS for styling -->\n <link rel=\"stylesheet\" href=\"https://cdn.jsdelivr.net/npm/purecss@3.0.0/build/pure-min.css\">\n </head>\n <body style=\"background-color: #f4f4f4;\">\n <div style=\"background-color: #fff; padding: 20px; margin: 20px;\">\n <h1>My Flask Data Dashboard</h1>\n {{ inline_datatable | safe }}\n </div>\n </body>\n </html>\n \"\"\",\n inline_datatable=string_datatable,\n )\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n```\n*Note: Pandas DataFrame indexes are not rendered by default. If you want to enable indexes in an HTML table, simply call `df2tables.render(df.reset_index(), args...)`*\n\n## Customization: Configuring DataTables from Python\n\nYou can now customize DataTables behavior directly from Python by passing configuration options through the new `js_opts` parameter. This allows you to control [DataTables options](https://datatables.net/reference/option/) and [features](https://datatables.net/reference/feature/) without modifying the HTML template. \n\n### Basic Usage\n\nSimply pass a dictionary of DataTables options to the `js_opts` parameter in the `render` or `render_inline` function.\n\n### Examples\n\n\n#### 1. Customize Layout and Language\n\nRearrange control elements (such as the search bar and info display) using the `layout` option, or localize text:\n```python\ncustom_cfg = {\n \"language\": {\n \"searchPlaceholder\": \"Search in all text columns\"\n },\n \"pageLength\": 25, # number of table rows showing\n \"layout\": {\n \"topStart\": \"info\",\n \"top1Start\": \"pageLength\",\n \"topEnd\": \"search\", \n \"bottomEnd\": \"paging\"\n }\n}\ndf2t.render(df, js_opts=custom_cfg, to_file=\"localized_table.html\")\n\n```\n#### 2. Replace Paging with Scrolling\n\nDisable pagination and enable vertical and horizontal scrolling for easier navigation of large datasets.\n\n_Note_: Using `scrollY` with disabled `paging` can be slow for large DataFrames.\n```python\nscroll_cfg = {\n \"paging\": False, # slow for large tables\n \"scrollCollapse\": False,\n \"scrollY\": '50vh', \n \"scrollX\": \"70vw\"\n}\ndf2t.render(df, js_opts=scroll_cfg, to_file=\"scrolling_table.html\")\n```\n\n\n\n### Error Handling\n\nInvalid keys are ignored by DataTables, so malformed or non-existent options **usually** will not break table rendering.\n\n**Note**: While invalid keys should be ignored, using a valid key with an incorrect *value type* may still cause an error in the browser's JavaScript console.\n\n### Available Configuration Options\n\n**Important Notes**\n\nSome configuration options require additional DataTables extensions or plugins (e.g., Buttons, FixedHeader). At this time, such plugin-dependent options are not yet supported.\n\nFor best results, start with core features such as `layout` or `language` options before exploring more advanced configurations.\n\nFor a complete list of available settings, refer to the official DataTables documentation and:\nhttps://datatables.net/examples/\n\nIt\u2019s best to start with the core DataTables Features before adding advanced configurations.\n\n * [Feature Reference](https://datatables.net/reference/feature/)\n * [Configuration Options Reference](https://datatables.net/reference/option/)\n * [Layout Configuration](https://datatables.net/reference/option/layout)\n * [Language configuration ](https://datatables.net/reference/option/language)\n\n\n### Column Name Formatting\n\nFor better readability in table headers, `df2tables` automatically converts underscores to spaces in column names. This improves word wrapping and prevents excessively wide columns.\n\nTo disable this automatic word wrapping behavior, add the following CSS to your custom template:\n\n```css\nspan.dt-column-title { \n white-space: nowrap; \n}\n```\n\n### Bulk Dataset Processing\n\nFor exploratory data analysis across multiple datasets, you can generate tables programmatically. \nThe example below uses the [vega_datasets](https://github.com/altair-viz/vega_datasets) package, which provides easy access to a variety of sample datasets commonly used in data visualization and analysis.\n\n*Note: Install vega_datasets with `pip install vega_datasets` to run this example.*\n\n[Quick Browse First 10 Vega Datasets](https://github.com/ts-kontakt/df2tables/blob/main/bulk_dataset_processing.py)\n\n\n\n### Error Handling\n\nThe module includes error handling for:\n\n- **JSON serialization**: Custom encoder handles complex pandas or Python data types\n- **Column compatibility**: Automatically converts problematic column types to string representation\n\n### Offline Usage\n*Note: \"Offline\" viewing assumes internet connectivity for CDN resources (DataTables, jQuery, PureCSS, [DataTables Column Control extension](https://datatables.net/extensions/columncontrol/)). For truly offline usage, modify the template to reference local copies of these libraries instead of CDN links.*\n\n## Appendix: Template Customization\n\nTemplates use [comnt](https://github.com/ts-kontakt/comnt), a minimal markup system based on HTML/JS comments.\n\n### Custom Templates\n\nCopy and modify `datatable_templ.html` to apply custom styling or libraries, then pass the new template path to `templ_path`.\n\n### Handle Pandas MultiIndex Columns (Experimental)\n\nMultiIndex columns are automatically flattened with underscore separation.\n\n## License\n\nMIT License \n\u00a9 Tomasz S\u0142ugocki\n",
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