A xlsx and html rendering library for rendering data available in Pandas DataFrames.
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README.rst

table-compositor

The table-compositor library provides the API to render data stored in table-like data structures. Currently the library supports rendering data available in a Panda's DataFrames. The DataFrame layout is used as the table layout(including single and multi hierarchical columns/indices) by the library. The table layout is rendered directly on to an XLSX sheet or to a HTML page. Styling and layout attributes can be used to render colorful XLSX or HTML reports. The library also supports rendering of multiple dataframes into a single XLSX sheet or HTML page. The objective of the library is to be able to use the DataFrame as the API to configure the style and layout properties of the report. Callback functions are provided to customize all styling properties. The nice thing about the callback functions are that the style properties are set on cells indexed with index/column values available in the original dataframe used during rendering.

Code: https://github.com/InvestmentSystems/table-compositor

Docs: http://table-compositor.readthedocs.io

Packages: https://pypi.python.org/pypi/table-compositor

Hello World of Reports

The purpose of this library is to use the Pandas DataFrame as an interface to represent the layout of a table that needs to be rendered to an xlsx file or as an html table. The library abstracts away the tedious work of working at the cell level of an xlsx sheet or a html table. It provides a call-back mechanism by which the user is able to provide values that need to be rendered and also the styling that needs to be used for each cell in the rendered table. The library is also capable of laying out multiple tables in the same sheet which are evenly spaced vertically or horizontally based on the layout configuration provided.

A Hello World Example: Dataframe to Xlsx

Every use of this library involves four steps.

  1. We build a dataframe that resembles the shape of the table that will be rendered.
  2. The dataframe is passed as an argument to the function called build_presentation_model. This function accepts a dataframe and also a number of functions as arguments. We call the value returned by this function, the presentation_model.
  3. Create a layout of multiple presentation models (if we want more than one table rendered in same xlsx sheet or same html page)
  4. Call the render_xlsx or render_html functions on the respective writers, XLXSWriter or HTMLWriter

A Quick Look at a Xlsx example

We will start with a simple dataframe and render the dataframe as is to an xlsx file

import pandas as pd
from table_compositor.table_compositor import build_presentation_model
from table_compositior.xlsx_writer import XLSXWriter

sample_df = pd.DataFrame(dict(a=[10, 20, 30, 40, 50], b=[0.1, 0.9,0.2, 0.6,0.3]), index=[1,2,3,4,5])

# create a presentation model
presentation_model = build_presentation_model(df=sample_df)

# create a layout, which is usually a nested list of presentation models
layout = [presentation_model]

# render to xlsx
output_fp = '/tmp/example1.xlsx'
XLSXWriter.to_xlsx(layout, output_fp=output_fp)

Running this code produces the following output:

doc/source/_static/xlsx_basic_example1.png

In the above code snippet, we first created a dataframe called sample_df.

To render this dataframe, we first invoke build_presentation_model. The build_presentation_model accepts the dataframe as its first argument. In this example, we use the defaults provided by this method for all other arguments. The build_presentation_model returns an presentation_model object.

Before we call XlSXWriter.to_xlsx we create a layout. A layout is a nested list of presenation_models. In our case, since we have only one presentation_model we create a list with a single element. Later on when we work with multiple presentation models that need to be rendered on to the same sheet, we could create nested list such as [[model1, model2], [model3]] etc.

Building the Presentation Model

The build_presentation_model function is the most important interface in this library. This function exposes all the functionality that is required to render beautiful looking excel worksheets or html tables.

We will now build up on our previous example and add styling to the report we generate. Before, we do that lets take a quick look at the signature of build_presentation__model.

package.public.table_compositor.table_compositor.table_compositor.build_presentation_model(df, output_format, data_value_func, data_style_func, header_style_func, header_value_func, index_style_func, index_value_func, index_name_func, index_name_style_func, **kwargs)[source]

Construct and return the presentation model that will be used while rendering to html/xlsx formats. The returned object has all the information required to render the tables in the requested format. The details of the object is transparent to the caller. It is only exposed for certain advanced operations.

Parameters:
  • df – The dataframe representation of the table. The shape of the dataframe closely resembles the table that will be rendered in the requested format.
  • output_format – ‘html’ or ‘xlsx’
  • data_value_func – example: lambda idx, col: df.loc[idx, col], assuming df is in the closure
  • data_style_func
    example: lambda idx, col: return dict(font=Font(...)),
    where Font is the openpyxl object and font is the attr available in the cell instance of openpyxl

    For xlsx, the keys in the dict are the attrs of the cell object in openpyxl and the values correspond to the value of that attribute. Example are found in xlsx_styles module.

    For html, the key-value pairs are any values that go into to the style attribute of a td, th cell in html. Examples are found in html_styles module. example: dict(background-color=’#F8F8F8’)

  • header_value_func – func that takes a object of type IndexNode. The IndexNode contains the attributes that refer to the header being rendered. The returned value from this function is displayed in place of the header in the dataframe at the location. The two properties available on the IndexNode object are value and key. The key is useful to identify the exact index and level in context while working with multi-hierarchical columns.
  • header_style_func – func that takes a object of type IndexNode. The return value of this function is similar to data_style_func.
  • index_value_func – func that takes a object of type IndexNode. The IndexNode contains the attributes that refer to the index being rendered. The returned value from this function is displayed in place of the index in the dataframe at the location.
  • index_style_func – func that takes a object of type IndexNode. The return value of this function is similar to data_style_func.
  • index_name_func – func that returns a string for index name (value to be displayed on top-left corner, above the index column)
  • index_name_style – the style value same as data_style_func that will be used to style the cell
  • kwargs

    ‘hide_index’ - if True, then hide the index column, default=False

    ‘hide_header, - if True, then hide the header, default=False

    ‘use_convert’ - if True, do some conversions from dataframe values to values excel can understand for example np.NaN are converted to NaN strings

Returns:

A presentation model, to be used to create layout and provide the layout to the html or xlsx writers.

About the callback functions provided as arguments:

Note that callback function provided as arguments to this function are provided with either a tuple of index, col arguments are some information regarding the index or headers being rendered. Therefore, a common pattern would be to capture the dataframe being rendered in a closure of this callback func before passing them as arugments.

For example:

df = pd.DataFrame(dict(a=[1, 2, 3]))

def data_value_func():
def _inner(idx, col):
return df.loc[idx, col] * 10.3

return _inner

pm = build_presentation_model(df=df, data_value_func=data_value_func())

Improving on our first iteration

Now, that we got a overview of the build_presentation_mode function, lets try setting these arguments to improve the look of our reports.

Say, we have the following requirements:

  1. Display column 'A' as in dollar format.
  2. Display column 'B' as percentage values.'
  3. Set back-ground color of column 'B' to red if value is less than 50%
  4. Capitalize all the column headers and add a yellow background
  5. Multiply all index values by 100 while rendering and add a color to the background.
  6. Display a 'custom text' on the top left corner, where pandas whole usually display the index name if available.

We update our previous example to do the following:

import os
import tempfile
import pandas as pd
from table_compositor.table_compositor import build_presentation_model
from table_compositor.xlsx_writer import XLSXWriter
from table_compositor.xlsx_styles import OpenPyxlStyleHelper
def basic_example2():

    df = pd.DataFrame(dict(a=[10, 20, 30, 40, 50], b=[0.1, 0.9,0.2, 0.6,0.3]), index=[1,2,3,4,5])

    def style_func(idx, col):
        if col == 'b':
            return OpenPyxlStyleHelper.get_style(number_format='0.00%')
        else:
            # for 'a' we do dollar format
            return OpenPyxlStyleHelper.get_style(number_format='$#,##.00')

    # create a presentation model
    # note the OpenPyxlStyleHelper function available in xlsx_styles module. But a return value of style function
    # can be any dict whose keys are attributes of the OpenPyxl cell object.
    presentation_model = build_presentation_model(
        df=df,
        data_value_func=lambda idx, col: df.loc[idx, col] * 10 if col == 'a' else df.loc[idx, col],
        data_style_func=style_func,
        header_value_func=lambda node: node.value.capitalize(),
        header_style_func=lambda _: OpenPyxlStyleHelper.default_header_style(),
        index_value_func=lambda node: node.value * 100,
        index_style_func=lambda _: OpenPyxlStyleHelper.default_header_style(),
        index_name_func=lambda _: 'Basic Example',
        index_name_style_func=lambda _: OpenPyxlStyleHelper.default_header_style())

    # create a layout, which is usually a nested list of presentation models
    layout = [presentation_model]

    # render to xlsx
    output_fp = os.path.join(tempfile.gettempdir(), 'basic_example2.xlsx')
    XLSXWriter.to_xlsx(layout, output_fp=output_fp)

On line 3 we create the dataframe.

To satisfy the requirements we listed above we pass the callback function to the build_presentation_model. Note that some helper functions are available in xlsx_style function to create styles for openpyxl. But, any other dict with keys that are attr of cell object of openpyxl should work. The above example produces the output as shown below:

doc/source/_static/xlsx_basic_example2.png

Multi-hierarchical columns and indices

Rendering dataframes with multi-hierarchical columns or indices are very similar to rendering the simpler dataframes. The data_value_func and data_style_func work the same way. The functions that handle index cell rendering and column header rendering can access the IndexNode object that is passed to those functions to determine the value and level that is currently being rendered. This becomes clearer with an example.

We demonstrate this by setting a variety of colors to each cell that holds one of the values of the hierarchical columns or indices.

Note that the IndexNode argument passed to the callback function has a node.key field that unique identifies each cell with a name that is built appending the value of each item in the index or column hierarchy.

import os
import tempfile
import pandas as pd
from table_compositor.table_compositor import build_presentation_model
from table_compositor.xlsx_writer import XLSXWriter
from table_compositor.xlsx_styles import OpenPyxlStyleHelper
def basic_example3():

    df = pd.DataFrame(dict(a=[10, 20, 30, 40],
                           b=[0.1, 0.9,0.2, 0.6],
                           d=[50, 60, 70, 80],
                           e=[200, 300, 400, 500]))
    df.columns = pd.MultiIndex.from_tuples([('A', 'x'), ('A', 'y'), ('B', 'x'), ('B', 'y')])
    df.index = pd.MultiIndex.from_tuples([(1, 100), (1, 200), (2, 100), (2, 200)])
    print(df)

    def index_style_func(node):
        # node.key here could be one of (1,), (1, 100), (2,), (2, 100), (2, 200)
        bg_color = 'FFFFFF'
        if node.key == (1,) or node.key == (2,):
            bg_color = '9E80B8'
        elif node.key[1] == 100:
            bg_color = '4F90C1'
        elif node.key[1] == 200:
            bg_color = '6DC066'
        return OpenPyxlStyleHelper.get_style(bg_color=bg_color)

    def header_style_func(node):
        bg_color = 'FFFFFF'
        if node.key == ('A',) or node.key == ('B',):
            bg_color = '9E80B8'
        elif node.key[1] == 'x':
            bg_color = '4F90C1'
        elif node.key[1] == 'y':
            bg_color = '6DC066'
        return OpenPyxlStyleHelper.get_style(bg_color=bg_color)

    # create a presentation model
    # note the OpenPyxlStyleHeloer function available in xlsx_styles module. But a return value of style function
    # can be any dict whose keys are attributes of the OpenPyxl cell object.
    presentation_model = build_presentation_model(
        df=df,
        index_style_func=index_style_func,
        header_style_func=header_style_func,
        index_name_func=lambda _: 'Multi-Hierarchy Example')

    # create a layout, which is usually a nested list of presentation models
    layout = [presentation_model]

    # render to xlsx
    output_fp = os.path.join(tempfile.gettempdir(), 'basic_example3.xlsx')
    XLSXWriter.to_xlsx(layout, output_fp=output_fp)

The above function gives us the xlsx file shown below. Note the colors used to render the indices and columns and review how the two functions, namely, index_style_function and header_style_function provide the colors based on the IndexNode attributes. You will notice the use of node.key in these functions to identify each cell uniquely.

doc/source/_static/xlsx_basic_example3.png

Layouts

Apart from providing styling and formatting facilities, the table compositor library also provides a powerful way to layour multiple tables on one sheet. Below you will see an sample rendering of 3 data frames rendered along-side each other using both horizontal and vertical orientations. Please refer to the [Layout](http://table-compositor.readthedocs.io/en/latest/layouts.html) documentation to learn more about layouts.

doc/source/_static/layout_example1_1.png

doc/source/_static/layout_example1_2.png

HTML Rendering

All the above rendering and layout capabilities we have seen above is also available for HTML rendering. The corresponding HTML rendering for XLSX examples we have seen above are provided below. Please refer to the [HTML Examples](http://table-compositor.readthedocs.io/en/latest/html_examples.html) to learn more about HTML rendering.

doc/source/_static/html_example1.png

doc/source/_static/html_example2.png

doc/source/_static/html_example3.png

doc/source/_static/html_example4.png