Metadata-Version: 2.1
Name: itables
Version: 0.3.0
Summary: Interactive Tables in Jupyter
Home-page: https://github.com/mwouts/itables
Author: Marc Wouts
Author-email: marc.wouts@gmail.com
License: MIT
Description: # Pandas DataFrames and Series as Interactive Tables in Jupyter
        
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        Turn pandas DataFrames and Series into interactive [datatables](https://datatables.net) in both your notebooks and their HTML representation with `import itables.interactive`:
        
        ![](https://raw.githubusercontent.com/mwouts/itables/master/demo/itables.gif)
        
        # Quick start
        
        Install the package with
        
        ```
        pip install itables
        ```
        
        Activate the interactive mode for all series and dataframes with
        
        ```python
        from itables import init_notebook_mode
        init_notebook_mode(all_interactive=True)
        ```
        
        ```python
        import world_bank_data as wb
        
        df = wb.get_countries()
        df
        ```
        
        You don't see any table above? Please either open the [HTML export](https://mwouts.github.io/itables/) of this notebook, or run this README on [Binder](https://mybinder.org/v2/gh/mwouts/itables/master?filepath=README.md)!
        
        
        Or display just one series or dataframe as an interactive table with the `show` function.
        
        ```python
        from itables import show
        
        x = wb.get_series("SP.POP.TOTL", mrv=1, simplify_index=True)
        show(x)
        ```
        
        # Advanced usage
        
        ## Pagination
        
        ### How many rows per page
        
        Select [how many entries](https://datatables.net/examples/advanced_init/length_menu.html) should appear at once in the table with either the `lengthMenu` argument of the `show` function, or with the global option `itables.options.lengthMenu`:
        
        ```python
        import itables.options as opt
        
        opt.lengthMenu = [2, 5, 10, 20, 50, 100, 200, 500]
        df
        ```
        
        ### Show the table in full
        
        Show the table in full with the [`paging` argument](https://datatables.net/reference/option/paging), either in the `show` method, or in the options:
        
        ```python
        show(df.head(), paging=False)
        ```
        
        ### Scroll
        
        If you prefer to replace the pagination with a [vertical scroll](https://datatables.net/examples/basic_init/scroll_y.html), use for instance
        
        ```python
        show(df, scrollY="200px", scrollCollapse=True, paging=False)
        ```
        
        ## Table and cell style
        
        Select how your table should look like with the `classes` argument of the `show` function, or by changing `itables.options.classes`. For the list of possible values, see [datatables' default style](https://datatables.net/manual/styling/classes) and [the style examples](https://datatables.net/examples/styling/).
        
        ```python
        opt.classes = ["display", "nowrap"]
        df
        ```
        
        ```python
        opt.classes = ["display", "cell-border"]
        df
        ```
        
        ## Float precision
        
        Floats are rounded using `pd.options.display.float_format`. Please change that format according to your preference.
        
        ```python
        import math
        import pandas as pd
        
        with pd.option_context("display.float_format", "{:,.2f}".format):
            show(pd.Series([i * math.pi for i in range(1, 6)]))
        ```
        
        You may also choose to convert floating numbers to strings:
        
        ```python
        with pd.option_context("display.float_format", "${:,.2f}".format):
            show(pd.Series([i * math.pi for i in range(1, 6)]))
        ```
        
        ## Advanced cell formatting
        
        Datatables allows to set the cell or row style depending on the cell content, with either the [createdRow](https://datatables.net/reference/option/createdRow) or [createdCell](https://datatables.net/reference/option/columns.createdCell) callback. For instance, if we want the cells with negative numbers to be colored in red, we can use the `columnDefs.createdCell` argument as follows:
        
        ```python
        show(
            pd.DataFrame([[-1, 2, -3, 4, -5], [6, -7, 8, -9, 10]], columns=list("abcde")),
            columnDefs=[
                {
                    "targets": "_all",
                    "createdCell": """function (td, cellData, rowData, row, col) {
              if ( cellData < 0 ) {
                $(td).css('color', 'red')
              }
            }""",
                }
            ],
        )
        ```
        
        ## Column width
        
        For tables that are larger than the notebook, the `columnDefs` argument allows to specify the desired width. If you wish you can also change the default in `itables.options`.
        
        ```python
        show(x.to_frame().T, columnDefs=[{"width": "120px", "targets": "_all"}])
        ```
        
        ## HTML in cells
        
        ```python
        import pandas as pd
        
        show(
            pd.Series(
                [
                    "<b>bold</b>",
                    "<i>italic</i>",
                    '<a href="https://github.com/mwouts/itables">link</a>',
                ],
                name="HTML",
            ),
            paging=False,
        )
        ```
        
        ## Select rows
        
        Not currently implemented. May be made available at a later stage using the [select](https://datatables.net/extensions/select/) extension for datatables.
        
        
        ## Copy, CSV, PDF and Excel buttons
        
        Not currently implemented. May be made available at a later stage thanks to the [buttons](https://datatables.net/extensions/buttons/) extension for datatable.
        
        
        ## <a name="downsampling"></a> Downsampling
        
        When the data in a table is larger than `maxBytes`, which is equal to 64KB by default, `itables` will display only a subset of the table - one that fits into `maxBytes`. If you wish, you can deactivate the limit with `maxBytes=0`, change the value of `maxBytes`, or similarly set a limit on the number of rows (`maxRows`, defaults to 0) or columns (`maxColumns`, defaults to `pd.get_option('display.max_columns')`).
        
        Note that datatables support [server-side processing](https://datatables.net/examples/data_sources/server_side). At a later stage we may implement support for larger tables using this feature.
        
        ```python
        df = wb.get_indicators().head(500)
        opt.maxBytes = 10000
        df.values.nbytes
        ```
        
        ```python
        df
        ```
        
        To show the table in full, we can modify the value of `maxBytes` either locally:
        
        ```python
        show(df, maxBytes=0)
        ```
        
        or globally:
        
        ```python
        opt.maxBytes = 2**20
        df
        ```
        
        # References
        
        ## DataTables
        
        - DataTables is a plug-in for the jQuery Javascript library. It has a great [documentation](https://datatables.net/manual/), and a large set of [examples](https://datatables.net/examples/index).
        - The R package [DT](https://rstudio.github.io/DT/) uses [datatables.net](https://datatables.net/) as the underlying library for both R notebooks and Shiny applications. In addition to the standard functionalities of the library (display, sort, filtering and row selection), RStudio seems to have implemented cell edition.
        - Marek Cermak has an interesting [tutorial](https://medium.com/@marekermk/guide-to-interactive-pandas-dataframe-representation-485acae02946) on how to use datatables within Jupyter. He also published [jupyter-datatables](https://github.com/CermakM/jupyter-datatables), with a focus on numerical data and distribution plots.
        
        ## Alternatives
        
        ITables uses basic Javascript, and because of this it will only work in Jupyter Notebook, not in JupyterLab. It is not a Jupyter widget, which means that it does not allows you to **edit** the content of the dataframe.
        
        If you are looking for Jupyter widgets, have a look at
        - [QGrid](https://github.com/quantopian/qgrid) by Quantopian
        - [IPyaggrid](https://dgothrek.gitlab.io/ipyaggrid/) by Louis Raison and Olivier Borderies
        - [IPySheet](https://github.com/QuantStack/ipysheet) by QuantStack.
        
        If you are looking for a table component that will fit in Dash applications, see [datatable by Dash](https://github.com/plotly/dash-table/).
        
        ## Contributing
        
        I think it would be very helpful to have an identical table component for both Jupyter and [Dash](http://dash.plot.ly/). Please [let us know](https://community.plot.ly/t/why-does-dash-have-its-own-datatable-library/) if you are interested in drafting a new table component based on an existing Javascript library for Dash.
        
        Also, if you happen to prefer another Javascript table library (say, [ag-grid](https://www.ag-grid.com/)), and you would like to see it supported in `itables`, please open either an issue or a PR, and let us know what is the minimal code to display a table in Jupyter using your library.
        
        <script async defer src="https://buttons.github.io/buttons.js"></script>
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Framework :: Jupyter
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/markdown
