<reponame>jtrim-ons/gptables
"""
Iris - Quick and Dirty Example
------------------------------

This example demonstrates use of the ``gptables.quick_and_dirty_workbook`` function.
This function is intended for use when only data, and no metadata, is required.
It should **not** be used for production of reference statistical tables for publication.

This function takes a list of ``pandas.DataFrame`` objects and writes each to a
separate sheet of the specified output `.xlsx` file. Formatting is taken from
the default ``gptables.Theme`` - gptheme - unless an alternative theme is provided
using the ``theme`` parameter. Column widths are automatically adjusted,
unless the ``auto_width`` parameter is set to False.

Unlike the other API functions, ``quick_and_dirty_workbook`` does not require
the user to specify table metadata or indicate which columns are row indexes.
Up to 3 row index levels are automatically detected. However, as with the other
API functions, row indices must be columns in the table instead of set as a ``pandas.Index``.
"""
import gptables as gpt
import pandas as pd
import numpy as np
import os
from pathlib import Path

## Read data and arrange
parent_dir = Path(__file__).parent

iris_data = pd.read_csv(parent_dir / "iris.csv")
iris_data = iris_data.loc[:, ["class", "sepal_length", "sepal_width"]]

iris_summary = iris_data.groupby("class").agg(np.mean)
iris_summary.index = [_[5:].capitalize() for _ in iris_summary.index]
iris_summary.rename(
    columns={
        "class":"class",
        "sepal_length":"Mean Sepal Length",
        "sepal_width":"Mean Sepal Width"
    }, 
    inplace=True
    )

# Drop index into table
iris_summary.reset_index(inplace=True)

# Make tables with varying numbers of index levels - up to 3 supported
iris_summary_0 = iris_summary.copy().iloc[:, 1:]

iris_summary_2 = iris_summary.copy()
iris_summary_2.insert(0, "top_index", ["A", "B", "C"])

iris_summary_3 = iris_summary_2.copy()
iris_summary_3.insert(2, "bottom_index", ["x", "y", "z"])


## Write using quick and dirty method
if __name__ == "__main__":
    output_path = parent_dir / "python_iris_quick_and_dirty.xlsx"
    gpt.quick_and_dirty_workbook(
        filename=output_path,
        tables=[iris_summary_0, iris_summary, iris_summary_2, iris_summary_3]
    )
    print("Output written at: ", output_path)


"""
Iris - Quick and Dirty Example
------------------------------

This example demonstrates use of the ``gptables.quick_and_dirty_workbook`` function.
This function is intended for use when only data, and no metadata, is required.
It should **not** be used for production of reference statistical tables for publication.

This function takes a list of ``pandas.DataFrame`` objects and writes each to a
separate sheet of the specified output `.xlsx` file. Formatting is taken from
the default ``gptables.Theme`` - gptheme - unless an alternative theme is provided
using the ``theme`` parameter. Column widths are automatically adjusted,
unless the ``auto_width`` parameter is set to False.

Unlike the other API functions, ``quick_and_dirty_workbook`` does not require
the user to specify table metadata or indicate which columns are row indexes.
Up to 3 row index levels are automatically detected. However, as with the other
API functions, row indices must be columns in the table instead of set as a ``pandas.Index``.
"""
import gptables as gpt
import pandas as pd
import numpy as np
import os
from pathlib import Path

## Read data and arrange
parent_dir = Path(__file__).parent

iris_data = pd.read_csv(parent_dir / "iris.csv")
iris_data = iris_data.loc[:, ["class", "sepal_length", "sepal_width"]]

iris_summary = iris_data.groupby("class").agg(np.mean)
iris_summary.index = [_[5:].capitalize() for _ in iris_summary.index]
iris_summary.rename(
    columns={
        "class":"class",
        "sepal_length":"Mean Sepal Length",
        "sepal_width":"Mean Sepal Width"
    }, 
    inplace=True
    )

# Drop index into table
iris_summary.reset_index(inplace=True)

# Make tables with varying numbers of index levels - up to 3 supported
iris_summary_0 = iris_summary.copy().iloc[:, 1:]

iris_summary_2 = iris_summary.copy()
iris_summary_2.insert(0, "top_index", ["A", "B", "C"])

iris_summary_3 = iris_summary_2.copy()
iris_summary_3.insert(2, "bottom_index", ["x", "y", "z"])


## Write using quick and dirty method
if __name__ == "__main__":
    output_path = parent_dir / "python_iris_quick_and_dirty.xlsx"
    gpt.quick_and_dirty_workbook(
        filename=output_path,
        tables=[iris_summary_0, iris_summary, iris_summary_2, iris_summary_3]
    )
    print("Output written at: ", output_path)
Iris - Quick and Dirty Example ------------------------------  This example demonstrates use of the ``gptables.quick_and_dirty_workbook`` function. This function is intended for use when only data, and no metadata, is required. It should **not** be used for production of reference statistical tables for publication.  This function takes a list of ``pandas.DataFrame`` objects and writes each to a separate sheet of the specified output `.xlsx` file. Formatting is taken from the default ``gptables.Theme`` - gptheme - unless an alternative theme is provided using the ``theme`` parameter. Column widths are automatically adjusted, unless the ``auto_width`` parameter is set to False.  Unlike the other API functions, ``quick_and_dirty_workbook`` does not require the user to specify table metadata or indicate which columns are row indexes. Up to 3 row index levels are automatically detected. However, as with the other API functions, row indices must be columns in the table instead of set as a ``pandas.Index``. ## Read data and arrange # Drop index into table # Make tables with varying numbers of index levels - up to 3 supported ## Write using quick and dirty method